miχPODS: New baseline#
TODO#
why does the stability diagram load multiple times
Is there an error in ONI phase labeling for the newer runs, presumably SST changed some.
Add diagnostic for convective “adjustment” or
diff == 1
Introduction#
Analysis of χpod shear mixing measurements recorded over approximately 15 years has revealed that mixing varies on multiple timescales, from hourly to interannual, in the tropical Pacific and Indian Oceans (Moum, 2021; Cherian et al., 2020b; Warner et al., 2016; Warner and Moum, 2019; Pujiana et al., 2018; Moum et al., 2009, 2013). These results show that equatorial mixing is organized in large-scale coherent patterns.
We pask whether these patterns, a decade-long observed mixing dataset (from χpods) to develop mixing process-oriented diagnostics (miχPODs) that assess the variability of parameterized equatorial mixing from monthly to decadal timescales; and its accumu- lated impact on model biases by applying miχPODs to a suite of existing CMIP6-class forced ocean and coupled model experiments from NCAR and GFDL.
Following Large and Gent (1999), the comparison of a 1D mixing model with LES or DNS is clean, because the forcing is the same, and direct, because the evaluation compares turbulence quantities (despite shortcomings such as model errors and idealized forcing). In contrast, the performance of a mixing scheme in global and regional model simulations is tested by comparing the simulated mean state to an observed mean state after a long integration (for e.g. Blanke and Delecluse, 1993; Robitaille and Weaver, 1995; Large and Gent, 1999; Gutjahr et al., 2020). This comparison is not clean, because of errors in forcing fields, nor is it direct, since mean state properties are not a direct output of the mixing scheme.
We propose diagnostics that evaluate the representation of the variability in parameterized equatorial mixing using the multiyear χpod turbulence dataset. While this comparison is not clean, it is direct, a substantial improvement to the current state-of-the-art approach.
These diagnostics are direct evaluations of the models’ ability to simulate the observed variability of ocean vertical mixing and reproduce large-scale patterns recorded in equatorial mixing observations.
Parameters#
TODO write down KPP and Jochum equations
TODO something about total diffusivity field in model. Total diffusivity \(K = K_D + K_{KPP}\). and mapping from physical process to parameterization.
We perturb the following parameters
“Background mixing” parameters used to simulate mixing by unresolved processes such as breaking internal waves.
A latitudinal varying background diffusivity \(K_D\) using the formulation by Jochum (2003)
A latitudinal varying background viscosity \(K_V'\) derived from \(K_D\) using a Prandtl number of 5 (CHECK).
A constant background viscosity \(K_V\) added to (2).
KPP Boundary layer scheme:
Bulk Richardson number criterion \(Ri_c\) which controls the depth of the actively mixing layer.
KPP shear mixing scheme:
Critical Richardson number \(Ri_0\) that controls when mixing occurs.
Maximum shear mixing viscosity \(ν_0\)
Choices for the parameter changes were influenced by experiments matching 1D KPP simulations to the Large Eddy Simulations of Whitt et al (2022). The range of parameter values explored here is not exhaustive and is only intended to demonstrate the utility of these metrics in tuning and assessing model performance.
Datasets#
Observations#
TAO \(T, S, u, v\) and turbulence (Moum et al XXX)
Note which depth χpods are being used
Add a figure showing sampling and gaps
Model Simulations#
The effect of these parameter changes are examined in a IPCC-class climate model: the development version of Community Earth System Model version 3 (CESM3), using the Modular Ocean Model version 6 (MOM6) ocean component, and the K Profile Paramerization vertical mixing scheme (Large et al., 1994).
For all simulations, we save a large number of variables every time step, which is an hour.
“old” baseline with KD=1e-5, KV=2e-4 (NCAR/MOM6#238)
old baseline +
kpp.lmd.004withKPP ν0=2.5, Ric=0.2, Ri0=0.5new baseline :
KD=0, KV=0new baseline :
kpp.lmd.004withKPP ν0=2.5, Ric=0.2, Ri0=0.5new baseline :
kpp.lmd.005withKPP ν0=2.5, Ric=0.3, Ri0=0.5
Summary#
Turning down the background visc by a factor of 40, (2e-4 → 5e-5 m2/s)
sharpens the EUC relative to TAO
changes the stability diagram for El-Nino warming phase.
Using
Ri_c=0.2, so shallower KPP surface layer, is key as Dan mentioned.
Setup#
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%load_ext autoreload
%load_ext rich
%load_ext watermark
%xmode minimal
import math
import warnings
import os
import cf_xarray as cfxr
import dcpy.datatree # noqa
import distributed
import holoviews as hv
import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
import tqdm
from datatree import DataTree
import xarray as xr
%aimport pump
from holoviews.plotting.bokeh.util import select_legends
from pump import mixpods
hv.notebook_extension("bokeh")
cfxr.set_options(warn_on_missing_variables=False)
xr.set_options(keep_attrs=True, display_expand_data=False)
plt.style.use("bmh")
plt.rcParams["figure.dpi"] = 140
# https://github.com/dask/distributed/issues/8022
import warnings
import bokeh
# warnings.simplefilter("ignore", bokeh.util.warnings.BokehUserWarning)
import datatree
%watermark -iv
Show code cell output Hide code cell output
The autoreload extension is already loaded. To reload it, use:
%reload_ext autoreload
Exception reporting mode: Minimal
/glade/u/home/dcherian/miniconda3/envs/pump/lib/python3.10/site-packages/dask_jobqueue/core.py:20: FutureWarning: tmpfile is deprecated and will be removed in a future release. Please use dask.utils.tmpfile instead.
from distributed.utils import tmpfile
holoviews : 1.17.0
distributed: 2023.8.0
dcpy : 0.1.dev387+gd06c937
xarray : 2023.7.0
pandas : 2.0.3
numpy : 1.24.4
bokeh : 3.2.2
datatree : 0.0.12
cf_xarray : 0.8.4
tqdm : 4.66.1
pump : 1.0+273.g892024e.dirty
matplotlib : 3.7.1
Show code cell source Hide code cell source
if "client" in locals():
client.close() # noqa
del client # noqa
if "cluster" in locals():
cluster.close() # noqa
import ncar_jobqueue
cluster = ncar_jobqueue.NCARCluster(
local_directory="/local_scratch/pbs.$PBS_JOBID/dask/spill",
)
cluster.adapt(minimum_jobs=2, maximum_jobs=8)
client = distributed.Client(cluster)
client
Show code cell output Hide code cell output
Client
Client-500a231d-3b92-11ee-a7a2-3cecef1b1260
| Connection method: Cluster object | Cluster type: dask_jobqueue.PBSCluster |
| Dashboard: https://jupyterhub.hpc.ucar.edu/stable/user/dcherian/casper/proxy/8787/status |
Cluster Info
PBSCluster
bd2779a5
| Dashboard: https://jupyterhub.hpc.ucar.edu/stable/user/dcherian/casper/proxy/8787/status | Workers: 0 |
| Total threads: 0 | Total memory: 0 B |
Scheduler Info
Scheduler
Scheduler-ffb8a918-2424-4fa1-ae87-90e7079370d9
| Comm: tcp://10.12.206.31:36719 | Workers: 0 |
| Dashboard: https://jupyterhub.hpc.ucar.edu/stable/user/dcherian/casper/proxy/8787/status | Total threads: 0 |
| Started: Just now | Total memory: 0 B |
Workers
Read#
MOM6#
Look at catalog#
The catalog only contains surface variables, full depth variables, NO SECTIONS YET.
catalog = pump.catalog.open_mom6_catalog()
catalog
pump-mom6-catalog catalog with 41 dataset(s) from 41 asset(s):
| unique | |
|---|---|
| casename | 11 |
| stream | 5 |
| path | 41 |
| baseline | 2 |
| levels | 2 |
| frequency | 3 |
| variables | 85 |
| shortname | 11 |
| description | 9 |
| derived_variables | 0 |
catalog.df["shortname"].unique()
array(['baseline', 'kpp.lmd.004', 'baseline.001', 'baseline.epbl.001',
'baseline.hb', 'baseline.kpp.lmd.002', 'baseline.kpp.lmd.003',
'baseline.kpp.lmd.004', 'new_baseline.hb',
'new_baseline.kpp.lmd.004', 'new_baseline.kpp.lmd.005'],
dtype=object)
Subset catalog#
Note
Use baseline.hb instead of baseline.001. This was run by Anna-Lena, and does not have gaps, and has heat budget output.
catalog_sub = pd.concat(
[
cat.df
for cat in [
# pick two "old" baseline simulations
catalog.search(
stream="combined",
levels=65,
shortname=["baseline.hb", "baseline.kpp.lmd.004"],
),
# and all new baseline simulations
catalog.search(stream="combined", levels=65, baseline="new"),
]
]
)
with pd.option_context("display.max_colwidth", None):
display(catalog_sub[["shortname", "casename", "path"]])
| shortname | casename | path | |
|---|---|---|---|
| 0 | baseline.hb | gmom.e23.GJRAv3.TL319_t061_zstar_N65.baseline.hb | /glade/campaign/cgd/oce/projects/pump/cesm/gmom.e23.GJRAv3.TL319_t061_zstar_N65.baseline.hb/run/jsons/combined.json |
| 1 | baseline.kpp.lmd.004 | gmom.e23.GJRAv3.TL319_t061_zstar_N65.baseline.kpp.lmd.004.mixpods | /glade/campaign/cgd/oce/projects/pump/cesm/gmom.e23.GJRAv3.TL319_t061_zstar_N65.baseline.kpp.lmd.004.mixpods/run/jsons/combined.json |
| 0 | new_baseline.hb | gmom.e23.GJRAv3.TL319_t061_zstar_N65.new_baseline.hb | /glade/campaign/cgd/oce/projects/pump/cesm/gmom.e23.GJRAv3.TL319_t061_zstar_N65.new_baseline.hb/run/jsons/combined.json |
| 1 | new_baseline.kpp.lmd.004 | gmom.e23.GJRAv3.TL319_t061_zstar_N65.new_baseline.kpp.lmd.004.mixpods | /glade/campaign/cgd/oce/projects/pump/cesm/gmom.e23.GJRAv3.TL319_t061_zstar_N65.new_baseline.kpp.lmd.004.mixpods/run/jsons/combined.json |
| 2 | new_baseline.kpp.lmd.005 | gmom.e23.GJRAv3.TL319_t061_zstar_N65.new_baseline.kpp.lmd.005.mixpods | /glade/campaign/cgd/oce/projects/pump/cesm/gmom.e23.GJRAv3.TL319_t061_zstar_N65.new_baseline.kpp.lmd.005.mixpods/run/jsons/combined.json |
backup#
This is what I was doing before switching to catalog.search
catalog_sub = {
k: catalog[k]
for k in catalog.keys()
if k == "kpp.lmd.004" or ("baseline" in k and "150" not in k)
}
display(catalog_sub)
{
'baseline': ('baseline', 'gmom.e23.GJRAv3.TL319_t061_zstar_N65.baseline.001.mixpods'),
'kpp.lmd.004': (
'KPP ν0=2.5, Ric=0.2, Ri0=0.5',
'gmom.e23.GJRAv3.TL319_t061_zstar_N65.baseline.kpp.lmd.004.mixpods'
),
'new_baseline.hb': ('KD=0, KV=0', 'gmom.e23.GJRAv3.TL319_t061_zstar_N65.new_baseline.hb'),
'new_baseline.kpp.lmd.004': (
'KPP ν0=2.5, Ric=0.2, Ri0=0.5',
'gmom.e23.GJRAv3.TL319_t061_zstar_N65.new_baseline.kpp.lmd.004.mixpods'
),
'new_baseline.kpp.lmd.005': (
'KPP ν0=2.5, Ri0=0.5',
'gmom.e23.GJRAv3.TL319_t061_zstar_N65.new_baseline.kpp.lmd.005.mixpods'
)
}
Since there are no sections in the catalog, and we do a bunch of custom things, loop over entries and load the section output.
%autoreload
datasets = {}
with warnings.catch_warnings():
warnings.simplefilter("ignore", category=UserWarning)
warnings.simplefilter("ignore", category=FutureWarning)
for _, (casename, shortname, description) in tqdm.tqdm(
catalog_sub[["casename", "shortname", "description"]].iterrows()
):
datasets.update(
{
shortname: mixpods.load_mom6_sections(casename).assign_attrs(
{"title": description}
)
}
)
0it [00:00, ?it/s]
using new oni
1it [00:02, 2.34s/it]
using new oni
2it [00:04, 2.15s/it]
using new oni
3it [00:06, 2.09s/it]
using new oni
4it [00:08, 1.97s/it]
using new oni
5it [00:09, 1.99s/it]
tree = DataTree.from_dict(datasets)
tree
<xarray.DatasetView>
Dimensions: ()
Data variables:
*empty*- time: 534360
- zl: 37
- zi: 37
- nv: 2
- nv(nv)float641.0 2.0
- long_name :
- vertex number
array([1., 2.])
- time(time)datetime64[ns]1958-01-01T00:30:00 ... 2018-12-...
array(['1958-01-01T00:30:00.000000000', '1958-01-01T01:30:00.000000000', '1958-01-01T02:30:00.000000000', ..., '2018-12-31T21:30:00.000000000', '2018-12-31T22:30:00.000000000', '2018-12-31T23:30:00.000000000'], dtype='datetime64[ns]') - xh()float64-140.0
- axis :
- X
- domain_decomposition :
- [220, 222, 220, 221]
- long_name :
- h point nominal longitude
- units :
- degrees_east
array(-140.)
- yh()float640.0625
- axis :
- Y
- domain_decomposition :
- [210, 258, 210, 221]
- long_name :
- h point nominal latitude
- units :
- degrees_north
array(0.06249997)
- yq()float64-0.0625
- axis :
- Y
- domain_decomposition :
- [209, 257, 209, 221]
- long_name :
- q point nominal latitude
- units :
- degrees_north
array(-0.06249997)
- zi(zi)float64-523.8 -481.0 -442.5 ... -2.5 -0.0
- axis :
- Z
- long_name :
- Interface pseudo-depth, -z*
- positive :
- up
- units :
- meter
array([-523.8 , -481.01, -442.51, -407.64, -375.88, -346.78, -319.99, -295.22, -272.22, -250.8 , -230.78, -212.02, -194.41, -177.85, -162.26, -147.57, -133.72, -120.66, -108.37, -96.83, -86.02, -75.94, -66.57, -57.91, -49.94, -42.66, -36.05, -30.1 , -24.81, -20.16, -16.15, -12.77, -10. , -7.5 , -5. , -2.5 , -0. ]) - zl(zl)float64-547.8 -502.4 ... -3.75 -1.25
- axis :
- Z
- long_name :
- Layer pseudo-depth, -z*
- positive :
- up
- units :
- meter
array([-547.75 , -502.405, -461.76 , -425.075, -391.76 , -361.33 , -333.385, -307.605, -283.72 , -261.51 , -240.79 , -221.4 , -203.215, -186.13 , -170.055, -154.915, -140.645, -127.19 , -114.515, -102.6 , -91.425, -80.98 , -71.255, -62.24 , -53.925, -46.3 , -39.355, -33.075, -27.455, -22.485, -18.155, -14.46 , -11.385, -8.75 , -6.25 , -3.75 , -1.25 ]) - eucmax(time)float64dask.array<chunksize=(8760,), meta=np.ndarray>
- units :
- m
- long_name :
- EUC maximum
- positive :
- up
Array Chunk Bytes 4.08 MiB 68.44 kiB Shape (534360,) (8760,) Dask graph 61 chunks in 21 graph layers Data type float64 numpy.ndarray - mldT(time)float64dask.array<chunksize=(8760,), meta=np.ndarray>
- long_name :
- MLD$_θ$
- units :
- m
- description :
- Interpolate θi to 1m grid. Search for max depth where |dθ| > 0.15
Array Chunk Bytes 4.08 MiB 68.44 kiB Shape (534360,) (8760,) Dask graph 61 chunks in 23 graph layers Data type float64 numpy.ndarray - dcl_mask(zi, time)booldask.array<chunksize=(37, 8760), meta=np.ndarray>
- description :
- True when 5m below mldT and above eucmax.
Array Chunk Bytes 18.86 MiB 316.52 kiB Shape (37, 534360) (37, 8760) Dask graph 61 chunks in 56 graph layers Data type bool numpy.ndarray - oni(time)float321.66 1.66 1.66 ... 0.81 0.81 0.81
array([1.66, 1.66, 1.66, ..., 0.81, 0.81, 0.81], dtype=float32)
- en_mask(time)boolTrue True True ... True True True
array([ True, True, True, ..., True, True, True])
- ln_mask(time)boolFalse False False ... False False
array([False, False, False, ..., False, False, False])
- warm_mask(time)boolTrue True True ... True True True
array([ True, True, True, ..., True, True, True])
- cool_mask(time)boolFalse False False ... False False
array([False, False, False, ..., False, False, False])
- enso_transition(time)<U12'El-Nino warm' ... 'El-Nino warm'
- description :
- Warner & Moum (2019) ENSO transition phase; El-Nino = ONI > 0.5 for at least 6 months; La-Nina = ONI < -0.5 for at least 6 months
array(['El-Nino warm', 'El-Nino warm', 'El-Nino warm', ..., 'El-Nino warm', 'El-Nino warm', 'El-Nino warm'], dtype='<U12')
- KPP_BulkRi(time, zl)float32dask.array<chunksize=(8760, 37), meta=np.ndarray>
- cell_measures :
- volume: volcello area: areacello
- cell_methods :
- area:mean zl:mean yh:mean xh:mean time: mean
- long_name :
- Bulk Richardson number used to find the OBL depth used by [CVMix] KPP
- time_avg_info :
- average_T1,average_T2,average_DT
- units :
- nondim
Array Chunk Bytes 75.42 MiB 1.24 MiB Shape (534360, 37) (8760, 37) Dask graph 61 chunks in 5 graph layers Data type float32 numpy.ndarray - KPP_N2(time, zi)float32dask.array<chunksize=(8760, 37), meta=np.ndarray>
- cell_measures :
- area: areacello
- cell_methods :
- area:mean zi:point yh:mean xh:mean time: mean
- long_name :
- Square of Brunt-Vaisala frequency used by [CVMix] KPP
- time_avg_info :
- average_T1,average_T2,average_DT
- units :
- 1/s2
Array Chunk Bytes 75.42 MiB 1.24 MiB Shape (534360, 37) (8760, 37) Dask graph 61 chunks in 5 graph layers Data type float32 numpy.ndarray - KPP_NLT_temp_budget(time, zl)float32dask.array<chunksize=(8760, 37), meta=np.ndarray>
- cell_measures :
- volume: volcello area: areacello
- cell_methods :
- area:mean zl:sum yh:mean xh:mean time: mean
- long_name :
- Heat content change due to non-local transport, as calculated by [CVMix] KPP
- time_avg_info :
- average_T1,average_T2,average_DT
- units :
- W m-2
Array Chunk Bytes 75.42 MiB 1.24 MiB Shape (534360, 37) (8760, 37) Dask graph 61 chunks in 5 graph layers Data type float32 numpy.ndarray - KPP_NLtransport_heat(time, zi)float32dask.array<chunksize=(8760, 37), meta=np.ndarray>
- cell_measures :
- area: areacello
- cell_methods :
- area:mean zi:point yh:mean xh:mean time: mean
- long_name :
- Non-local transport (Cs*G(sigma)) for heat, as calculated by [CVMix] KPP
- time_avg_info :
- average_T1,average_T2,average_DT
- units :
- nondim
Array Chunk Bytes 75.42 MiB 1.24 MiB Shape (534360, 37) (8760, 37) Dask graph 61 chunks in 5 graph layers Data type float32 numpy.ndarray - KPP_OBLdepth(time)float32dask.array<chunksize=(534360,), meta=np.ndarray>
- cell_measures :
- area: areacello
- cell_methods :
- area:mean yh:mean xh:mean time: mean
- long_name :
- Thickness of the surface Ocean Boundary Layer calculated by [CVMix] KPP
- time_avg_info :
- average_T1,average_T2,average_DT
- units :
- meter
Array Chunk Bytes 2.04 MiB 2.04 MiB Shape (534360,) (534360,) Dask graph 1 chunks in 3 graph layers Data type float32 numpy.ndarray - KPP_buoyFlux(time, zi)float32dask.array<chunksize=(8760, 37), meta=np.ndarray>
- cell_measures :
- area: areacello
- cell_methods :
- area:mean zi:point yh:mean xh:mean time: mean
- long_name :
- Surface (and penetrating) buoyancy flux, as used by [CVMix] KPP
- time_avg_info :
- average_T1,average_T2,average_DT
- units :
- m2/s3
Array Chunk Bytes 75.42 MiB 1.24 MiB Shape (534360, 37) (8760, 37) Dask graph 61 chunks in 5 graph layers Data type float32 numpy.ndarray - KPP_kheat(time, zi)float32dask.array<chunksize=(8760, 37), meta=np.ndarray>
- cell_measures :
- area: areacello
- cell_methods :
- area:mean zi:point yh:mean xh:mean time: mean
- long_name :
- Heat diffusivity due to KPP, as calculated by [CVMix] KPP
- time_avg_info :
- average_T1,average_T2,average_DT
- units :
- m2/s
Array Chunk Bytes 75.42 MiB 1.24 MiB Shape (534360, 37) (8760, 37) Dask graph 61 chunks in 5 graph layers Data type float32 numpy.ndarray - Kd_heat(time, zi)float32dask.array<chunksize=(8760, 37), meta=np.ndarray>
- cell_measures :
- area: areacello
- cell_methods :
- area:mean zi:point yh:mean xh:mean time: mean
- long_name :
- Total diapycnal diffusivity for heat at interfaces
- standard_name :
- ocean_vertical_heat_diffusivity
- time_avg_info :
- average_T1,average_T2,average_DT
- units :
- m2 s-1
Array Chunk Bytes 75.42 MiB 1.24 MiB Shape (534360, 37) (8760, 37) Dask graph 61 chunks in 5 graph layers Data type float32 numpy.ndarray - Kv_u(time, zl)float32dask.array<chunksize=(8760, 37), meta=np.ndarray>
- cell_methods :
- zl:mean yh:mean xq:point time: mean
- interp_method :
- none
- long_name :
- Total vertical viscosity at u-points
- standard_name :
- ocean_vertical_x_viscosity
- time_avg_info :
- average_T1,average_T2,average_DT
- units :
- m2 s-1
Array Chunk Bytes 75.42 MiB 1.24 MiB Shape (534360, 37) (8760, 37) Dask graph 61 chunks in 5 graph layers Data type float32 numpy.ndarray - Kv_v(time, zl)float32dask.array<chunksize=(8760, 37), meta=np.ndarray>
- cell_methods :
- zl:mean yq:point xh:mean time: mean
- interp_method :
- none
- long_name :
- Total vertical viscosity at v-points
- standard_name :
- ocean_vertical_y_viscosity
- time_avg_info :
- average_T1,average_T2,average_DT
- units :
- m2 s-1
Array Chunk Bytes 75.42 MiB 1.24 MiB Shape (534360, 37) (8760, 37) Dask graph 61 chunks in 5 graph layers Data type float32 numpy.ndarray - N2(time, zi)float32dask.array<chunksize=(8760, 37), meta=np.ndarray>
- cell_measures :
- area: areacello
- cell_methods :
- area:mean zi:point yh:mean xh:mean time: mean
- long_name :
- Buoyancy frequency squared
- time_avg_info :
- average_T1,average_T2,average_DT
- units :
- s-2
Array Chunk Bytes 75.42 MiB 1.24 MiB Shape (534360, 37) (8760, 37) Dask graph 61 chunks in 5 graph layers Data type float32 numpy.ndarray - N2_shear(time, zi)float32dask.array<chunksize=(8760, 37), meta=np.ndarray>
- cell_measures :
- area: areacello
- cell_methods :
- area:mean zi:point yh:mean xh:mean time: mean
- long_name :
- Square of Brunt-Vaisala frequency used by MOM_CVMix_shear module
- time_avg_info :
- average_T1,average_T2,average_DT
- units :
- 1/s2
Array Chunk Bytes 75.42 MiB 1.24 MiB Shape (534360, 37) (8760, 37) Dask graph 61 chunks in 5 graph layers Data type float32 numpy.ndarray - S2_shear(time, zi)float32dask.array<chunksize=(8760, 37), meta=np.ndarray>
- cell_measures :
- area: areacello
- cell_methods :
- area:mean zi:point yh:mean xh:mean time: mean
- long_name :
- Square of vertical shear used by MOM_CVMix_shear module
- time_avg_info :
- average_T1,average_T2,average_DT
- units :
- 1/s2
Array Chunk Bytes 75.42 MiB 1.24 MiB Shape (534360, 37) (8760, 37) Dask graph 61 chunks in 5 graph layers Data type float32 numpy.ndarray - SSH(time)float32dask.array<chunksize=(534360,), meta=np.ndarray>
- cell_measures :
- area: areacello
- cell_methods :
- area:mean yh:mean xh:mean time: mean
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- units :
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Array Chunk Bytes 2.04 MiB 2.04 MiB Shape (534360,) (534360,) Dask graph 1 chunks in 3 graph layers Data type float32 numpy.ndarray - SW(time)float32dask.array<chunksize=(534360,), meta=np.ndarray>
- cell_measures :
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- cell_methods :
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- long_name :
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- standard_name :
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- units :
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Array Chunk Bytes 2.04 MiB 2.04 MiB Shape (534360,) (534360,) Dask graph 1 chunks in 3 graph layers Data type float32 numpy.ndarray - SW_pen(time)float32dask.array<chunksize=(534360,), meta=np.ndarray>
- cell_measures :
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- time_avg_info :
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- units :
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Array Chunk Bytes 2.04 MiB 2.04 MiB Shape (534360,) (534360,) Dask graph 1 chunks in 3 graph layers Data type float32 numpy.ndarray - T_advection_xy(time, zl)float32dask.array<chunksize=(8760, 37), meta=np.ndarray>
- cell_measures :
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- cell_methods :
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- long_name :
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- units :
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Array Chunk Bytes 75.42 MiB 1.24 MiB Shape (534360, 37) (8760, 37) Dask graph 61 chunks in 5 graph layers Data type float32 numpy.ndarray - T_lbdxy_cont_tendency(time, zl)float32dask.array<chunksize=(8760, 37), meta=np.ndarray>
- cell_measures :
- volume: volcello area: areacello
- cell_methods :
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- long_name :
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- time_avg_info :
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- units :
- W m-2
Array Chunk Bytes 75.42 MiB 1.24 MiB Shape (534360, 37) (8760, 37) Dask graph 61 chunks in 5 graph layers Data type float32 numpy.ndarray - Tflx_dia_diff(time, zi)float32dask.array<chunksize=(8760, 37), meta=np.ndarray>
- cell_measures :
- area: areacello
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- area:mean zi:point yh:mean xh:mean time: mean
- long_name :
- Diffusive diapycnal temperature flux across interfaces
- standard_name :
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- time_avg_info :
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- units :
- degC m s-1
Array Chunk Bytes 75.42 MiB 1.24 MiB Shape (534360, 37) (8760, 37) Dask graph 61 chunks in 5 graph layers Data type float32 numpy.ndarray - Th_tendency_vert_remap(time, zl)float32dask.array<chunksize=(8760, 37), meta=np.ndarray>
- cell_measures :
- volume: volcello area: areacello
- cell_methods :
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- long_name :
- Vertical remapping tracer content tendency for Heat
- time_avg_info :
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- units :
- W m-2
Array Chunk Bytes 75.42 MiB 1.24 MiB Shape (534360, 37) (8760, 37) Dask graph 61 chunks in 5 graph layers Data type float32 numpy.ndarray - boundary_forcing_heat_tendency(time, zl)float32dask.array<chunksize=(8760, 37), meta=np.ndarray>
- cell_measures :
- volume: volcello area: areacello
- cell_methods :
- area:mean zl:sum yh:mean xh:mean time: mean
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- time_avg_info :
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- units :
- W m-2
Array Chunk Bytes 75.42 MiB 1.24 MiB Shape (534360, 37) (8760, 37) Dask graph 61 chunks in 5 graph layers Data type float32 numpy.ndarray - dens(time, zl)float32dask.array<chunksize=(8760, 37), meta=np.ndarray>
- cell_measures :
- volume: volcello area: areacello
- cell_methods :
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- long_name :
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- units :
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Array Chunk Bytes 75.42 MiB 1.24 MiB Shape (534360, 37) (8760, 37) Dask graph 61 chunks in 5 graph layers Data type float32 numpy.ndarray - densT(time, zl)float32dask.array<chunksize=(8760, 37), meta=np.ndarray>
- cell_measures :
- volume: volcello area: areacello
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- long_name :
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- units :
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Array Chunk Bytes 75.42 MiB 1.24 MiB Shape (534360, 37) (8760, 37) Dask graph 61 chunks in 5 graph layers Data type float32 numpy.ndarray - frazil_heat_tendency(time, zl)float32dask.array<chunksize=(8760, 37), meta=np.ndarray>
- cell_measures :
- volume: volcello area: areacello
- cell_methods :
- area:mean zl:sum yh:mean xh:mean time: mean
- long_name :
- Heat tendency due to frazil formation
- time_avg_info :
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- units :
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Array Chunk Bytes 75.42 MiB 1.24 MiB Shape (534360, 37) (8760, 37) Dask graph 61 chunks in 5 graph layers Data type float32 numpy.ndarray - h(time, zl)float32dask.array<chunksize=(8760, 37), meta=np.ndarray>
- cell_measures :
- volume: volcello area: areacello
- cell_methods :
- area:mean zl:sum yh:mean xh:mean time: mean
- long_name :
- Layer Thickness
- time_avg_info :
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- units :
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Array Chunk Bytes 75.42 MiB 1.24 MiB Shape (534360, 37) (8760, 37) Dask graph 61 chunks in 5 graph layers Data type float32 numpy.ndarray - mlotst(time)float32dask.array<chunksize=(534360,), meta=np.ndarray>
- cell_measures :
- area: areacello
- cell_methods :
- area:mean yh:mean xh:mean time: mean
- long_name :
- Ocean Mixed Layer Thickness Defined by Sigma T
- standard_name :
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- time_avg_info :
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- units :
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Array Chunk Bytes 2.04 MiB 2.04 MiB Shape (534360,) (534360,) Dask graph 1 chunks in 3 graph layers Data type float32 numpy.ndarray - net_heat_surface(time)float32dask.array<chunksize=(534360,), meta=np.ndarray>
- cell_measures :
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- cell_methods :
- area:mean yh:mean xh:mean time: mean
- long_name :
- Surface ocean heat flux from SW+LW+lat+sens+mass transfer+frazil+restore+seaice_melt_heat or flux adjustments
- standard_name :
- surface_downward_heat_flux_in_sea_water
- time_avg_info :
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- units :
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Array Chunk Bytes 2.04 MiB 2.04 MiB Shape (534360,) (534360,) Dask graph 1 chunks in 3 graph layers Data type float32 numpy.ndarray - opottempdiff(time, zl)float32dask.array<chunksize=(8760, 37), meta=np.ndarray>
- cell_measures :
- volume: volcello area: areacello
- cell_methods :
- area:mean zl:sum yh:mean xh:mean time: mean
- long_name :
- Tendency of sea water potential temperature expressed as heat content due to parameterized dianeutral mixing
- standard_name :
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- time_avg_info :
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- units :
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Array Chunk Bytes 75.42 MiB 1.24 MiB Shape (534360, 37) (8760, 37) Dask graph 61 chunks in 5 graph layers Data type float32 numpy.ndarray - opottemppmdiff(time, zl)float32dask.array<chunksize=(8760, 37), meta=np.ndarray>
- cell_measures :
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- cell_methods :
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- long_name :
- Tendency of sea water potential temperature expressed as heat content due to parameterized mesoscale neutral diffusion
- standard_name :
- tendency_of_sea_water_potential_temperature_expressed_as_heat_content_due_to_parameterized_mesoscale_neutral_diffusion
- time_avg_info :
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- units :
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Array Chunk Bytes 75.42 MiB 1.24 MiB Shape (534360, 37) (8760, 37) Dask graph 61 chunks in 5 graph layers Data type float32 numpy.ndarray - opottemptend(time, zl)float32dask.array<chunksize=(8760, 37), meta=np.ndarray>
- cell_measures :
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- cell_methods :
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- long_name :
- Tendency of Sea Water Potential Temperature Expressed as Heat Content
- standard_name :
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- time_avg_info :
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- units :
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Array Chunk Bytes 75.42 MiB 1.24 MiB Shape (534360, 37) (8760, 37) Dask graph 61 chunks in 5 graph layers Data type float32 numpy.ndarray - ri_grad_shear(time, zi)float32dask.array<chunksize=(8760, 37), meta=np.ndarray>
- cell_measures :
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- cell_methods :
- area:mean zi:point yh:mean xh:mean time: mean
- long_name :
- Gradient Richarson number used by MOM_CVMix_shear module
- time_avg_info :
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- units :
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Array Chunk Bytes 75.42 MiB 1.24 MiB Shape (534360, 37) (8760, 37) Dask graph 61 chunks in 5 graph layers Data type float32 numpy.ndarray - ri_grad_shear_orig(time, zi)float32dask.array<chunksize=(8760, 37), meta=np.ndarray>
- cell_measures :
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- long_name :
- Original gradient Richarson number, before smoothing was applied. This is part of the MOM_CVMix_shear module and only available
- time_avg_info :
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- units :
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Array Chunk Bytes 75.42 MiB 1.24 MiB Shape (534360, 37) (8760, 37) Dask graph 61 chunks in 5 graph layers Data type float32 numpy.ndarray - so(time, zl)float32dask.array<chunksize=(8760, 37), meta=np.ndarray>
- cell_measures :
- volume: volcello area: areacello
- cell_methods :
- area:mean zl:mean yh:mean xh:mean time: mean
- long_name :
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- units :
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Array Chunk Bytes 75.42 MiB 1.24 MiB Shape (534360, 37) (8760, 37) Dask graph 61 chunks in 5 graph layers Data type float32 numpy.ndarray - taux(time)float32dask.array<chunksize=(534360,), meta=np.ndarray>
- cell_methods :
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- interp_method :
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- standard_name :
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- time_avg_info :
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- units :
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Array Chunk Bytes 2.04 MiB 2.04 MiB Shape (534360,) (534360,) Dask graph 1 chunks in 3 graph layers Data type float32 numpy.ndarray - tauy(time)float32dask.array<chunksize=(534360,), meta=np.ndarray>
- cell_methods :
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- interp_method :
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- time_avg_info :
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- units :
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Array Chunk Bytes 2.04 MiB 2.04 MiB Shape (534360,) (534360,) Dask graph 1 chunks in 3 graph layers Data type float32 numpy.ndarray - thetao(time, zl)float32dask.array<chunksize=(8760, 37), meta=np.ndarray>
- cell_measures :
- volume: volcello area: areacello
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- long_name :
- Sea Water Potential Temperature
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- units :
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Array Chunk Bytes 75.42 MiB 1.24 MiB Shape (534360, 37) (8760, 37) Dask graph 61 chunks in 5 graph layers Data type float32 numpy.ndarray - uo(time, zl)float32dask.array<chunksize=(8760, 37), meta=np.ndarray>
- cell_methods :
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- interp_method :
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- long_name :
- Sea Water X Velocity
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- time_avg_info :
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- units :
- m s-1
Array Chunk Bytes 75.42 MiB 1.24 MiB Shape (534360, 37) (8760, 37) Dask graph 61 chunks in 5 graph layers Data type float32 numpy.ndarray - vo(time, zl)float32dask.array<chunksize=(8760, 37), meta=np.ndarray>
- cell_methods :
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- interp_method :
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- Sea Water Y Velocity
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- time_avg_info :
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- units :
- m s-1
Array Chunk Bytes 75.42 MiB 1.24 MiB Shape (534360, 37) (8760, 37) Dask graph 61 chunks in 5 graph layers Data type float32 numpy.ndarray - volcello(time, zl)float32dask.array<chunksize=(8760, 37), meta=np.ndarray>
- cell_methods :
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- long_name :
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- standard_name :
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- time_avg_info :
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- units :
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Array Chunk Bytes 75.42 MiB 1.24 MiB Shape (534360, 37) (8760, 37) Dask graph 61 chunks in 5 graph layers Data type float32 numpy.ndarray - zos(time)float32dask.array<chunksize=(534360,), meta=np.ndarray>
- cell_measures :
- area: areacello
- cell_methods :
- area:mean yh:mean xh:mean time: mean
- long_name :
- Sea surface height above geoid
- standard_name :
- sea_surface_height_above_geoid
- time_avg_info :
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- units :
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Array Chunk Bytes 2.04 MiB 2.04 MiB Shape (534360,) (534360,) Dask graph 1 chunks in 3 graph layers Data type float32 numpy.ndarray - α(time, zl)float32dask.array<chunksize=(8760, 37), meta=np.ndarray>
- standard_name :
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- units :
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Array Chunk Bytes 75.42 MiB 1.24 MiB Shape (534360, 37) (8760, 37) Dask graph 61 chunks in 5 graph layers Data type float32 numpy.ndarray - β(time, zl)float32dask.array<chunksize=(8760, 37), meta=np.ndarray>
- standard_name :
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- units :
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Array Chunk Bytes 75.42 MiB 1.24 MiB Shape (534360, 37) (8760, 37) Dask graph 61 chunks in 5 graph layers Data type float32 numpy.ndarray - Tz(time, zi)float32dask.array<chunksize=(8760, 36), meta=np.ndarray>
- long_name :
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- units :
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Array Chunk Bytes 75.42 MiB 1.20 MiB Shape (534360, 37) (8760, 36) Dask graph 122 chunks in 23 graph layers Data type float32 numpy.ndarray - Sz(time, zi)float32dask.array<chunksize=(8760, 36), meta=np.ndarray>
- long_name :
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- units :
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Array Chunk Bytes 75.42 MiB 1.20 MiB Shape (534360, 37) (8760, 36) Dask graph 122 chunks in 23 graph layers Data type float32 numpy.ndarray - N2T(time, zi)float32dask.array<chunksize=(8760, 36), meta=np.ndarray>
- long_name :
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- units :
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Array Chunk Bytes 75.42 MiB 1.20 MiB Shape (534360, 37) (8760, 36) Dask graph 122 chunks in 27 graph layers Data type float32 numpy.ndarray - S2(time, zi)float32dask.array<chunksize=(8760, 36), meta=np.ndarray>
- long_name :
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- units :
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Array Chunk Bytes 75.42 MiB 1.20 MiB Shape (534360, 37) (8760, 36) Dask graph 122 chunks in 50 graph layers Data type float32 numpy.ndarray - shred2(time, zi)float32dask.array<chunksize=(8760, 36), meta=np.ndarray>
- long_name :
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- units :
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Array Chunk Bytes 75.42 MiB 1.20 MiB Shape (534360, 37) (8760, 36) Dask graph 122 chunks in 66 graph layers Data type float32 numpy.ndarray - Rig_T(time, zi)float32dask.array<chunksize=(8760, 36), meta=np.ndarray>
- long_name :
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Array Chunk Bytes 75.42 MiB 1.20 MiB Shape (534360, 37) (8760, 36) Dask graph 122 chunks in 65 graph layers Data type float32 numpy.ndarray - Rig(time, zi)float32dask.array<chunksize=(8760, 36), meta=np.ndarray>
- cell_measures :
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Array Chunk Bytes 75.42 MiB 1.20 MiB Shape (534360, 37) (8760, 36) Dask graph 122 chunks in 55 graph layers Data type float32 numpy.ndarray - tau(time)float32dask.array<chunksize=(534360,), meta=np.ndarray>
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- units :
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Array Chunk Bytes 2.04 MiB 2.04 MiB Shape (534360,) (534360,) Dask graph 1 chunks in 9 graph layers Data type float32 numpy.ndarray - Jb(time, zi)float32dask.array<chunksize=(8760, 36), meta=np.ndarray>
- cell_measures :
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- long_name :
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- standard_name :
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- units :
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Array Chunk Bytes 75.42 MiB 1.20 MiB Shape (534360, 37) (8760, 36) Dask graph 122 chunks in 62 graph layers Data type float32 numpy.ndarray - Jq(time, zi)float64dask.array<chunksize=(8760, 37), meta=np.ndarray>
- units :
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- long_name :
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Array Chunk Bytes 150.84 MiB 2.47 MiB Shape (534360, 37) (8760, 37) Dask graph 61 chunks in 6 graph layers Data type float64 numpy.ndarray - ν(time, zl)float32dask.array<chunksize=(8760, 37), meta=np.ndarray>
- cell_methods :
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- interp_method :
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- time_avg_info :
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- units :
- m2 s-1
Array Chunk Bytes 75.42 MiB 1.24 MiB Shape (534360, 37) (8760, 37) Dask graph 61 chunks in 5 graph layers Data type float32 numpy.ndarray - shear_prod(time, zi)float32dask.array<chunksize=(8760, 36), meta=np.ndarray>
- long_name :
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- units :
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Array Chunk Bytes 75.42 MiB 1.20 MiB Shape (534360, 37) (8760, 36) Dask graph 122 chunks in 71 graph layers Data type float32 numpy.ndarray - eps(time, zi)float32dask.array<chunksize=(8760, 36), meta=np.ndarray>
- long_name :
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- units :
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Array Chunk Bytes 75.42 MiB 1.20 MiB Shape (534360, 37) (8760, 36) Dask graph 122 chunks in 121 graph layers Data type float32 numpy.ndarray - chi(time, zi)float32dask.array<chunksize=(8760, 36), meta=np.ndarray>
- long_name :
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- units :
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Array Chunk Bytes 75.42 MiB 1.20 MiB Shape (534360, 37) (8760, 36) Dask graph 122 chunks in 30 graph layers Data type float32 numpy.ndarray - Rif(time, zi)float32dask.array<chunksize=(8760, 36), meta=np.ndarray>
- cell_measures :
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- long_name :
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Array Chunk Bytes 75.42 MiB 1.20 MiB Shape (534360, 37) (8760, 36) Dask graph 122 chunks in 122 graph layers Data type float32 numpy.ndarray - sst(time)float32dask.array<chunksize=(8760,), meta=np.ndarray>
- cell_measures :
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- long_name :
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- units :
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Array Chunk Bytes 2.04 MiB 34.22 kiB Shape (534360,) (8760,) Dask graph 61 chunks in 5 graph layers Data type float32 numpy.ndarray
- title :
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<xarray.DatasetView> Dimensions: (time: 534360, zl: 37, zi: 37, nv: 2) Coordinates: (12/16) * nv (nv) float64 1.0 2.0 * time (time) datetime64[ns] 1958-01-01T00:30:00... xh float64 -140.0 yh float64 0.0625 yq float64 -0.0625 * zi (zi) float64 -523.8 -481.0 ... -2.5 -0.0 ... ... oni (time) float32 1.66 1.66 1.66 ... 0.81 0.81 en_mask (time) bool True True True ... True True ln_mask (time) bool False False ... False False warm_mask (time) bool True True True ... True True cool_mask (time) bool False False ... False False enso_transition (time) <U12 'El-Nino warm' ... 'El-Nino w... Data variables: (12/58) KPP_BulkRi (time, zl) float32 dask.array<chunksize=(8760, 37), meta=np.ndarray> KPP_N2 (time, zi) float32 dask.array<chunksize=(8760, 37), meta=np.ndarray> KPP_NLT_temp_budget (time, zl) float32 dask.array<chunksize=(8760, 37), meta=np.ndarray> KPP_NLtransport_heat (time, zi) float32 dask.array<chunksize=(8760, 37), meta=np.ndarray> KPP_OBLdepth (time) float32 dask.array<chunksize=(534360,), meta=np.ndarray> KPP_buoyFlux (time, zi) float32 dask.array<chunksize=(8760, 37), meta=np.ndarray> ... ... ν (time, zl) float32 dask.array<chunksize=(8760, 37), meta=np.ndarray> shear_prod (time, zi) float32 dask.array<chunksize=(8760, 36), meta=np.ndarray> eps (time, zi) float32 dask.array<chunksize=(8760, 36), meta=np.ndarray> chi (time, zi) float32 dask.array<chunksize=(8760, 36), meta=np.ndarray> Rif (time, zi) float32 dask.array<chunksize=(8760, 36), meta=np.ndarray> sst (time) float32 dask.array<chunksize=(8760,), meta=np.ndarray> Attributes: title: baselinebaseline.hb- time: 218424
- zi: 37
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- description :
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array(['____________', '____________', '____________', ..., '____________', '____________', '____________'], dtype='<U12')
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- cell_measures :
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Array Chunk Bytes 30.83 MiB 1.24 MiB Shape (218424, 37) (8760, 37) Dask graph 25 chunks in 5 graph layers Data type float32 numpy.ndarray - KPP_NLT_temp_budget(time, zl)float32dask.array<chunksize=(8760, 37), meta=np.ndarray>
- cell_measures :
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- units :
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Array Chunk Bytes 30.83 MiB 1.24 MiB Shape (218424, 37) (8760, 37) Dask graph 25 chunks in 5 graph layers Data type float32 numpy.ndarray - KPP_NLtransport_heat(time, zi)float32dask.array<chunksize=(8760, 37), meta=np.ndarray>
- cell_measures :
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- long_name :
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- units :
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Array Chunk Bytes 30.83 MiB 1.24 MiB Shape (218424, 37) (8760, 37) Dask graph 25 chunks in 5 graph layers Data type float32 numpy.ndarray - KPP_OBLdepth(time)float32dask.array<chunksize=(218424,), meta=np.ndarray>
- cell_measures :
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- long_name :
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- time_avg_info :
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- units :
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- cell_measures :
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Array Chunk Bytes 30.83 MiB 1.24 MiB Shape (218424, 37) (8760, 37) Dask graph 25 chunks in 5 graph layers Data type float32 numpy.ndarray - KPP_kheat(time, zi)float32dask.array<chunksize=(8760, 37), meta=np.ndarray>
- cell_measures :
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- long_name :
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Array Chunk Bytes 30.83 MiB 1.24 MiB Shape (218424, 37) (8760, 37) Dask graph 25 chunks in 5 graph layers Data type float32 numpy.ndarray - Kd_heat(time, zi)float32dask.array<chunksize=(8760, 37), meta=np.ndarray>
- cell_measures :
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- long_name :
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- units :
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Array Chunk Bytes 30.83 MiB 1.24 MiB Shape (218424, 37) (8760, 37) Dask graph 25 chunks in 5 graph layers Data type float32 numpy.ndarray - Kv_u(time, zl)float32dask.array<chunksize=(8760, 37), meta=np.ndarray>
- cell_methods :
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- interp_method :
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- units :
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Array Chunk Bytes 30.83 MiB 1.24 MiB Shape (218424, 37) (8760, 37) Dask graph 25 chunks in 5 graph layers Data type float32 numpy.ndarray - Kv_v(time, zl)float32dask.array<chunksize=(8760, 37), meta=np.ndarray>
- cell_methods :
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Array Chunk Bytes 30.83 MiB 1.24 MiB Shape (218424, 37) (8760, 37) Dask graph 25 chunks in 5 graph layers Data type float32 numpy.ndarray - N2(time, zi)float32dask.array<chunksize=(8760, 37), meta=np.ndarray>
- cell_measures :
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- units :
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Array Chunk Bytes 30.83 MiB 1.24 MiB Shape (218424, 37) (8760, 37) Dask graph 25 chunks in 5 graph layers Data type float32 numpy.ndarray - N2_shear(time, zi)float32dask.array<chunksize=(8760, 37), meta=np.ndarray>
- cell_measures :
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- units :
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Array Chunk Bytes 30.83 MiB 1.24 MiB Shape (218424, 37) (8760, 37) Dask graph 25 chunks in 5 graph layers Data type float32 numpy.ndarray - S2_shear(time, zi)float32dask.array<chunksize=(8760, 37), meta=np.ndarray>
- cell_measures :
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- long_name :
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- time_avg_info :
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- units :
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Array Chunk Bytes 30.83 MiB 1.24 MiB Shape (218424, 37) (8760, 37) Dask graph 25 chunks in 5 graph layers Data type float32 numpy.ndarray - SW(time)float32dask.array<chunksize=(218424,), meta=np.ndarray>
- cell_measures :
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- cell_methods :
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- long_name :
- Shortwave radiation flux into ocean
- standard_name :
- net_downward_shortwave_flux_at_sea_water_surface
- time_avg_info :
- average_T1,average_T2,average_DT
- units :
- W m-2
Array Chunk Bytes 853.22 kiB 853.22 kiB Shape (218424,) (218424,) Dask graph 1 chunks in 3 graph layers Data type float32 numpy.ndarray - T_advection_xy(time, zl)float32dask.array<chunksize=(8760, 37), meta=np.ndarray>
- cell_measures :
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- cell_methods :
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- long_name :
- Horizontal convergence of residual mean advective fluxes of heat
- time_avg_info :
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- units :
- W m-2
Array Chunk Bytes 30.83 MiB 1.24 MiB Shape (218424, 37) (8760, 37) Dask graph 25 chunks in 5 graph layers Data type float32 numpy.ndarray - T_lbdxy_cont_tendency(time, zl)float32dask.array<chunksize=(8760, 37), meta=np.ndarray>
- cell_measures :
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- long_name :
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- time_avg_info :
- average_T1,average_T2,average_DT
- units :
- W m-2
Array Chunk Bytes 30.83 MiB 1.24 MiB Shape (218424, 37) (8760, 37) Dask graph 25 chunks in 5 graph layers Data type float32 numpy.ndarray - Tflx_dia_diff(time, zi)float32dask.array<chunksize=(8760, 37), meta=np.ndarray>
- cell_measures :
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- cell_methods :
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- long_name :
- Diffusive diapycnal temperature flux across interfaces
- time_avg_info :
- average_T1,average_T2,average_DT
- units :
- degC m s-1
- standard_name :
- ocean_vertical_diffusive_heat_flux
Array Chunk Bytes 30.83 MiB 1.24 MiB Shape (218424, 37) (8760, 37) Dask graph 25 chunks in 5 graph layers Data type float32 numpy.ndarray - Th_tendency_vert_remap(time, zl)float32dask.array<chunksize=(8760, 37), meta=np.ndarray>
- cell_measures :
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- cell_methods :
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- long_name :
- Vertical remapping tracer content tendency for Heat
- time_avg_info :
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- units :
- W m-2
Array Chunk Bytes 30.83 MiB 1.24 MiB Shape (218424, 37) (8760, 37) Dask graph 25 chunks in 5 graph layers Data type float32 numpy.ndarray - boundary_forcing_heat_tendency(time, zl)float32dask.array<chunksize=(8760, 37), meta=np.ndarray>
- cell_measures :
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- cell_methods :
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- long_name :
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- time_avg_info :
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- units :
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Array Chunk Bytes 30.83 MiB 1.24 MiB Shape (218424, 37) (8760, 37) Dask graph 25 chunks in 5 graph layers Data type float32 numpy.ndarray - dens(time, zl)float32dask.array<chunksize=(8760, 37), meta=np.ndarray>
- cell_measures :
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- cell_methods :
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- long_name :
- Sea Water Salinity
- standard_name :
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- time_avg_info :
- average_T1,average_T2,average_DT
- units :
- kg/m^3
Array Chunk Bytes 30.83 MiB 1.24 MiB Shape (218424, 37) (8760, 37) Dask graph 25 chunks in 5 graph layers Data type float32 numpy.ndarray - densT(time, zl)float32dask.array<chunksize=(8760, 37), meta=np.ndarray>
- cell_measures :
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- cell_methods :
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- long_name :
- Sea Water Potential Temperature
- standard_name :
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- time_avg_info :
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- units :
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Array Chunk Bytes 30.83 MiB 1.24 MiB Shape (218424, 37) (8760, 37) Dask graph 25 chunks in 5 graph layers Data type float32 numpy.ndarray - frazil_heat_tendency(time, zl)float32dask.array<chunksize=(8760, 37), meta=np.ndarray>
- cell_measures :
- volume: volcello area: areacello
- cell_methods :
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- long_name :
- Heat tendency due to frazil formation
- time_avg_info :
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- units :
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Array Chunk Bytes 30.83 MiB 1.24 MiB Shape (218424, 37) (8760, 37) Dask graph 25 chunks in 5 graph layers Data type float32 numpy.ndarray - h(time, zl)float32dask.array<chunksize=(8760, 37), meta=np.ndarray>
- cell_measures :
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- cell_methods :
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- long_name :
- Layer Thickness
- time_avg_info :
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- units :
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Array Chunk Bytes 30.83 MiB 1.24 MiB Shape (218424, 37) (8760, 37) Dask graph 25 chunks in 5 graph layers Data type float32 numpy.ndarray - mlotst(time)float32dask.array<chunksize=(218424,), meta=np.ndarray>
- cell_measures :
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- cell_methods :
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- long_name :
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- standard_name :
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- time_avg_info :
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- units :
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Array Chunk Bytes 853.22 kiB 853.22 kiB Shape (218424,) (218424,) Dask graph 1 chunks in 3 graph layers Data type float32 numpy.ndarray - net_heat_surface(time)float32dask.array<chunksize=(218424,), meta=np.ndarray>
- cell_measures :
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- cell_methods :
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- long_name :
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- standard_name :
- surface_downward_heat_flux_in_sea_water
- time_avg_info :
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- units :
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Array Chunk Bytes 853.22 kiB 853.22 kiB Shape (218424,) (218424,) Dask graph 1 chunks in 3 graph layers Data type float32 numpy.ndarray - opottempdiff(time, zl)float32dask.array<chunksize=(8760, 37), meta=np.ndarray>
- cell_measures :
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- long_name :
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- standard_name :
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- time_avg_info :
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- units :
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Array Chunk Bytes 30.83 MiB 1.24 MiB Shape (218424, 37) (8760, 37) Dask graph 25 chunks in 5 graph layers Data type float32 numpy.ndarray - opottemppmdiff(time, zl)float32dask.array<chunksize=(8760, 37), meta=np.ndarray>
- cell_measures :
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- long_name :
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- standard_name :
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- units :
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Array Chunk Bytes 30.83 MiB 1.24 MiB Shape (218424, 37) (8760, 37) Dask graph 25 chunks in 5 graph layers Data type float32 numpy.ndarray - opottemptend(time, zl)float32dask.array<chunksize=(8760, 37), meta=np.ndarray>
- cell_measures :
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- long_name :
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- time_avg_info :
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- units :
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Array Chunk Bytes 30.83 MiB 1.24 MiB Shape (218424, 37) (8760, 37) Dask graph 25 chunks in 5 graph layers Data type float32 numpy.ndarray - ri_grad_shear(time, zi)float32dask.array<chunksize=(8760, 37), meta=np.ndarray>
- cell_measures :
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- long_name :
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- time_avg_info :
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- units :
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Array Chunk Bytes 30.83 MiB 1.24 MiB Shape (218424, 37) (8760, 37) Dask graph 25 chunks in 5 graph layers Data type float32 numpy.ndarray - so(time, zl)float32dask.array<chunksize=(8760, 37), meta=np.ndarray>
- cell_measures :
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- long_name :
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- units :
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Array Chunk Bytes 30.83 MiB 1.24 MiB Shape (218424, 37) (8760, 37) Dask graph 25 chunks in 5 graph layers Data type float32 numpy.ndarray - taux(time)float32dask.array<chunksize=(218424,), meta=np.ndarray>
- cell_methods :
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- units :
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Array Chunk Bytes 853.22 kiB 853.22 kiB Shape (218424,) (218424,) Dask graph 1 chunks in 3 graph layers Data type float32 numpy.ndarray - tauy(time)float32dask.array<chunksize=(218424,), meta=np.ndarray>
- cell_methods :
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Array Chunk Bytes 853.22 kiB 853.22 kiB Shape (218424,) (218424,) Dask graph 1 chunks in 3 graph layers Data type float32 numpy.ndarray - thetao(time, zl)float32dask.array<chunksize=(8760, 37), meta=np.ndarray>
- cell_measures :
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Array Chunk Bytes 30.83 MiB 1.24 MiB Shape (218424, 37) (8760, 37) Dask graph 25 chunks in 5 graph layers Data type float32 numpy.ndarray - uo(time, zl)float32dask.array<chunksize=(8760, 37), meta=np.ndarray>
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Array Chunk Bytes 30.83 MiB 1.24 MiB Shape (218424, 37) (8760, 37) Dask graph 25 chunks in 5 graph layers Data type float32 numpy.ndarray - vo(time, zl)float32dask.array<chunksize=(8760, 37), meta=np.ndarray>
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- units :
- m s-1
Array Chunk Bytes 30.83 MiB 1.24 MiB Shape (218424, 37) (8760, 37) Dask graph 25 chunks in 5 graph layers Data type float32 numpy.ndarray - volcello(time, zl)float32dask.array<chunksize=(8760, 37), meta=np.ndarray>
- cell_methods :
- area:sum zl:sum yh:sum xh:sum time: mean
- long_name :
- Ocean grid-cell volume
- standard_name :
- ocean_volume
- time_avg_info :
- average_T1,average_T2,average_DT
- units :
- m3
Array Chunk Bytes 30.83 MiB 1.24 MiB Shape (218424, 37) (8760, 37) Dask graph 25 chunks in 5 graph layers Data type float32 numpy.ndarray - zos(time)float32dask.array<chunksize=(218424,), meta=np.ndarray>
- cell_measures :
- area: areacello
- cell_methods :
- area:mean yh:mean xh:mean time: mean
- long_name :
- Sea surface height above geoid
- standard_name :
- sea_surface_height_above_geoid
- time_avg_info :
- average_T1,average_T2,average_DT
- units :
- m
Array Chunk Bytes 853.22 kiB 853.22 kiB Shape (218424,) (218424,) Dask graph 1 chunks in 3 graph layers Data type float32 numpy.ndarray - α(time, zl)float32dask.array<chunksize=(8760, 37), meta=np.ndarray>
- standard_name :
- sea_water_thermal_expansion_coefficient
- units :
- C-1
Array Chunk Bytes 30.83 MiB 1.24 MiB Shape (218424, 37) (8760, 37) Dask graph 25 chunks in 5 graph layers Data type float32 numpy.ndarray - β(time, zl)float32dask.array<chunksize=(8760, 37), meta=np.ndarray>
- standard_name :
- sea_water_haline_contraction_coefficient
- units :
- kg/g
Array Chunk Bytes 30.83 MiB 1.24 MiB Shape (218424, 37) (8760, 37) Dask graph 25 chunks in 5 graph layers Data type float32 numpy.ndarray - Tz(time, zi)float32dask.array<chunksize=(8760, 36), meta=np.ndarray>
- long_name :
- $T_z$
- units :
- Cm$^{-1}$
Array Chunk Bytes 30.83 MiB 1.20 MiB Shape (218424, 37) (8760, 36) Dask graph 50 chunks in 23 graph layers Data type float32 numpy.ndarray - Sz(time, zi)float32dask.array<chunksize=(8760, 36), meta=np.ndarray>
- long_name :
- $S_z$
- units :
- m$^{-1}$
Array Chunk Bytes 30.83 MiB 1.20 MiB Shape (218424, 37) (8760, 36) Dask graph 50 chunks in 23 graph layers Data type float32 numpy.ndarray - N2T(time, zi)float32dask.array<chunksize=(8760, 36), meta=np.ndarray>
- long_name :
- $N_T^2$
- units :
- s$^{-2}$
Array Chunk Bytes 30.83 MiB 1.20 MiB Shape (218424, 37) (8760, 36) Dask graph 50 chunks in 27 graph layers Data type float32 numpy.ndarray - S2(time, zi)float32dask.array<chunksize=(8760, 36), meta=np.ndarray>
- long_name :
- $S^2$
- units :
- s$^{-2}$
Array Chunk Bytes 30.83 MiB 1.20 MiB Shape (218424, 37) (8760, 36) Dask graph 50 chunks in 50 graph layers Data type float32 numpy.ndarray - shred2(time, zi)float32dask.array<chunksize=(8760, 36), meta=np.ndarray>
- long_name :
- $Sh_{red}^2$
- units :
- $s^{-2}$
Array Chunk Bytes 30.83 MiB 1.20 MiB Shape (218424, 37) (8760, 36) Dask graph 50 chunks in 66 graph layers Data type float32 numpy.ndarray - Rig_T(time, zi)float32dask.array<chunksize=(8760, 36), meta=np.ndarray>
- long_name :
- $Ri^g_T$
Array Chunk Bytes 30.83 MiB 1.20 MiB Shape (218424, 37) (8760, 36) Dask graph 50 chunks in 65 graph layers Data type float32 numpy.ndarray - Rig(time, zi)float32dask.array<chunksize=(8760, 36), meta=np.ndarray>
- cell_measures :
- area: areacello
- cell_methods :
- area:mean zi:point yh:mean xh:mean time: mean
- long_name :
- $Ri^g$
- time_avg_info :
- average_T1,average_T2,average_DT
Array Chunk Bytes 30.83 MiB 1.20 MiB Shape (218424, 37) (8760, 36) Dask graph 50 chunks in 55 graph layers Data type float32 numpy.ndarray - tau(time)float32dask.array<chunksize=(218424,), meta=np.ndarray>
- cell_methods :
- yh:mean xq:point time: mean
- interp_method :
- none
- long_name :
- Zonal surface stress from ocean interactions with atmos and ice
- time_avg_info :
- average_T1,average_T2,average_DT
- units :
- Pa
Array Chunk Bytes 853.22 kiB 853.22 kiB Shape (218424,) (218424,) Dask graph 1 chunks in 9 graph layers Data type float32 numpy.ndarray - Jb(time, zi)float32dask.array<chunksize=(8760, 36), meta=np.ndarray>
- cell_measures :
- area: areacello
- cell_methods :
- area:mean zi:point yh:mean xh:mean time: mean
- long_name :
- Total diapycnal diffusivity for heat at interfaces
- standard_name :
- ocean_vertical_diffusive_buoyancy_flux
- time_avg_info :
- average_T1,average_T2,average_DT
- units :
- m2 s-1
Array Chunk Bytes 30.83 MiB 1.20 MiB Shape (218424, 37) (8760, 36) Dask graph 50 chunks in 62 graph layers Data type float32 numpy.ndarray - Jq(time, zi)float64dask.array<chunksize=(8760, 37), meta=np.ndarray>
- units :
- W/m^2
- long_name :
- $J_q^t$
Array Chunk Bytes 61.66 MiB 2.47 MiB Shape (218424, 37) (8760, 37) Dask graph 25 chunks in 6 graph layers Data type float64 numpy.ndarray - ν(time, zl)float32dask.array<chunksize=(8760, 37), meta=np.ndarray>
- cell_methods :
- zl:mean yh:mean xq:point time: mean
- interp_method :
- none
- long_name :
- Total vertical viscosity at u-points
- standard_name :
- ocean_vertical_momentum_diffusivity
- time_avg_info :
- average_T1,average_T2,average_DT
- units :
- m2 s-1
Array Chunk Bytes 30.83 MiB 1.24 MiB Shape (218424, 37) (8760, 37) Dask graph 25 chunks in 5 graph layers Data type float32 numpy.ndarray - shear_prod(time, zi)float32dask.array<chunksize=(8760, 36), meta=np.ndarray>
- long_name :
- $SP$
- units :
- W/kg
Array Chunk Bytes 30.83 MiB 1.20 MiB Shape (218424, 37) (8760, 36) Dask graph 50 chunks in 71 graph layers Data type float32 numpy.ndarray - eps(time, zi)float32dask.array<chunksize=(8760, 36), meta=np.ndarray>
- long_name :
- $ε$
- units :
- W/kg
Array Chunk Bytes 30.83 MiB 1.20 MiB Shape (218424, 37) (8760, 36) Dask graph 50 chunks in 121 graph layers Data type float32 numpy.ndarray - chi(time, zi)float32dask.array<chunksize=(8760, 36), meta=np.ndarray>
- long_name :
- $χ$
- units :
- C^2/s
Array Chunk Bytes 30.83 MiB 1.20 MiB Shape (218424, 37) (8760, 36) Dask graph 50 chunks in 30 graph layers Data type float32 numpy.ndarray - Rif(time, zi)float32dask.array<chunksize=(8760, 36), meta=np.ndarray>
- cell_measures :
- area: areacello
- cell_methods :
- area:mean zi:point yh:mean xh:mean time: mean
- long_name :
- Total diapycnal diffusivity for heat at interfaces
- standard_name :
- flux_richardson_number
- time_avg_info :
- average_T1,average_T2,average_DT
- units :
- m2 s-1
Array Chunk Bytes 30.83 MiB 1.20 MiB Shape (218424, 37) (8760, 36) Dask graph 50 chunks in 122 graph layers Data type float32 numpy.ndarray - sst(time)float32dask.array<chunksize=(8760,), meta=np.ndarray>
- cell_measures :
- volume: volcello area: areacello
- cell_methods :
- area:mean zl:mean yh:mean xh:mean time: mean
- long_name :
- $SST$
- standard_name :
- sea_surface_temperature
- time_avg_info :
- average_T1,average_T2,average_DT
- units :
- degC
Array Chunk Bytes 853.22 kiB 34.22 kiB Shape (218424,) (8760,) Dask graph 25 chunks in 5 graph layers Data type float32 numpy.ndarray
- title :
- KPP ν0=2.5, Ric=0.2, Ri0=0.5
<xarray.DatasetView> Dimensions: (time: 218424, zi: 37, zl: 37, nv: 2) Coordinates: (12/16) * nv (nv) float64 1.0 2.0 * time (time) datetime64[ns] 2003-01-07T00:30:00... xh float64 -140.0 yh float64 0.0625 yq float64 -0.0625 * zi (zi) float64 -523.8 -481.0 ... -2.5 -0.0 ... ... oni (time) float32 0.63 0.63 0.63 ... nan nan en_mask (time) bool False False ... False False ln_mask (time) bool False False ... False False warm_mask (time) bool True True True ... True True cool_mask (time) bool False False ... False False enso_transition (time) <U12 '____________' ... '_________... Data variables: (12/54) KPP_N2 (time, zi) float32 dask.array<chunksize=(8760, 37), meta=np.ndarray> KPP_NLT_temp_budget (time, zl) float32 dask.array<chunksize=(8760, 37), meta=np.ndarray> KPP_NLtransport_heat (time, zi) float32 dask.array<chunksize=(8760, 37), meta=np.ndarray> KPP_OBLdepth (time) float32 dask.array<chunksize=(218424,), meta=np.ndarray> KPP_buoyFlux (time, zi) float32 dask.array<chunksize=(8760, 37), meta=np.ndarray> KPP_kheat (time, zi) float32 dask.array<chunksize=(8760, 37), meta=np.ndarray> ... ... ν (time, zl) float32 dask.array<chunksize=(8760, 37), meta=np.ndarray> shear_prod (time, zi) float32 dask.array<chunksize=(8760, 36), meta=np.ndarray> eps (time, zi) float32 dask.array<chunksize=(8760, 36), meta=np.ndarray> chi (time, zi) float32 dask.array<chunksize=(8760, 36), meta=np.ndarray> Rif (time, zi) float32 dask.array<chunksize=(8760, 36), meta=np.ndarray> sst (time) float32 dask.array<chunksize=(8760,), meta=np.ndarray> Attributes: title: KPP ν0=2.5, Ric=0.2, Ri0=0.5baseline.kpp.lmd.004- time: 534360
- zl: 37
- zi: 37
- nv: 2
- nv(nv)float641.0 2.0
- long_name :
- vertex number
array([1., 2.])
- time(time)datetime64[ns]1958-01-01T00:30:00 ... 2018-12-...
array(['1958-01-01T00:30:00.000000000', '1958-01-01T01:30:00.000000000', '1958-01-01T02:30:00.000000000', ..., '2018-12-31T21:30:00.000000000', '2018-12-31T22:30:00.000000000', '2018-12-31T23:30:00.000000000'], dtype='datetime64[ns]') - xh()float64-140.0
- axis :
- X
- domain_decomposition :
- [220, 222, 220, 221]
- long_name :
- h point nominal longitude
- units :
- degrees_east
array(-140.)
- yh()float640.0625
- axis :
- Y
- domain_decomposition :
- [210, 258, 210, 221]
- long_name :
- h point nominal latitude
- units :
- degrees_north
array(0.06249997)
- yq()float64-0.0625
- axis :
- Y
- domain_decomposition :
- [209, 257, 209, 221]
- long_name :
- q point nominal latitude
- units :
- degrees_north
array(-0.06249997)
- zi(zi)float64-523.8 -481.0 -442.5 ... -2.5 -0.0
- axis :
- Z
- long_name :
- Interface pseudo-depth, -z*
- positive :
- up
- units :
- meter
array([-523.8 , -481.01, -442.51, -407.64, -375.88, -346.78, -319.99, -295.22, -272.22, -250.8 , -230.78, -212.02, -194.41, -177.85, -162.26, -147.57, -133.72, -120.66, -108.37, -96.83, -86.02, -75.94, -66.57, -57.91, -49.94, -42.66, -36.05, -30.1 , -24.81, -20.16, -16.15, -12.77, -10. , -7.5 , -5. , -2.5 , -0. ]) - zl(zl)float64-547.8 -502.4 ... -3.75 -1.25
- axis :
- Z
- long_name :
- Layer pseudo-depth, -z*
- positive :
- up
- units :
- meter
array([-547.75 , -502.405, -461.76 , -425.075, -391.76 , -361.33 , -333.385, -307.605, -283.72 , -261.51 , -240.79 , -221.4 , -203.215, -186.13 , -170.055, -154.915, -140.645, -127.19 , -114.515, -102.6 , -91.425, -80.98 , -71.255, -62.24 , -53.925, -46.3 , -39.355, -33.075, -27.455, -22.485, -18.155, -14.46 , -11.385, -8.75 , -6.25 , -3.75 , -1.25 ]) - eucmax(time)float64dask.array<chunksize=(8760,), meta=np.ndarray>
- units :
- m
- long_name :
- EUC maximum
- positive :
- up
Array Chunk Bytes 4.08 MiB 68.44 kiB Shape (534360,) (8760,) Dask graph 61 chunks in 21 graph layers Data type float64 numpy.ndarray - mldT(time)float64dask.array<chunksize=(8760,), meta=np.ndarray>
- long_name :
- MLD$_θ$
- units :
- m
- description :
- Interpolate θi to 1m grid. Search for max depth where |dθ| > 0.15
Array Chunk Bytes 4.08 MiB 68.44 kiB Shape (534360,) (8760,) Dask graph 61 chunks in 23 graph layers Data type float64 numpy.ndarray - dcl_mask(zi, time)booldask.array<chunksize=(37, 8760), meta=np.ndarray>
- description :
- True when 5m below mldT and above eucmax.
Array Chunk Bytes 18.86 MiB 316.52 kiB Shape (37, 534360) (37, 8760) Dask graph 61 chunks in 56 graph layers Data type bool numpy.ndarray - oni(time)float321.66 1.66 1.66 ... 0.81 0.81 0.81
array([1.66, 1.66, 1.66, ..., 0.81, 0.81, 0.81], dtype=float32)
- en_mask(time)boolTrue True True ... True True True
array([ True, True, True, ..., True, True, True])
- ln_mask(time)boolFalse False False ... False False
array([False, False, False, ..., False, False, False])
- warm_mask(time)boolTrue True True ... True True True
array([ True, True, True, ..., True, True, True])
- cool_mask(time)boolFalse False False ... False False
array([False, False, False, ..., False, False, False])
- enso_transition(time)<U12'El-Nino warm' ... 'El-Nino warm'
- description :
- Warner & Moum (2019) ENSO transition phase; El-Nino = ONI > 0.5 for at least 6 months; La-Nina = ONI < -0.5 for at least 6 months
array(['El-Nino warm', 'El-Nino warm', 'El-Nino warm', ..., 'El-Nino warm', 'El-Nino warm', 'El-Nino warm'], dtype='<U12')
- KPP_BulkRi(time, zl)float32dask.array<chunksize=(8760, 37), meta=np.ndarray>
- cell_measures :
- volume: volcello area: areacello
- cell_methods :
- area:mean zl:mean yh:mean xh:mean time: mean
- long_name :
- Bulk Richardson number used to find the OBL depth used by [CVMix] KPP
- time_avg_info :
- average_T1,average_T2,average_DT
- units :
- nondim
Array Chunk Bytes 75.42 MiB 1.24 MiB Shape (534360, 37) (8760, 37) Dask graph 61 chunks in 5 graph layers Data type float32 numpy.ndarray - KPP_NLtransport_heat(time, zi)float32dask.array<chunksize=(8760, 37), meta=np.ndarray>
- cell_measures :
- area: areacello
- cell_methods :
- area:mean zi:point yh:mean xh:mean time: mean
- long_name :
- Non-local transport (Cs*G(sigma)) for heat, as calculated by [CVMix] KPP
- time_avg_info :
- average_T1,average_T2,average_DT
- units :
- nondim
Array Chunk Bytes 75.42 MiB 1.24 MiB Shape (534360, 37) (8760, 37) Dask graph 61 chunks in 5 graph layers Data type float32 numpy.ndarray - KPP_OBLdepth(time)float32dask.array<chunksize=(534360,), meta=np.ndarray>
- cell_measures :
- area: areacello
- cell_methods :
- area:mean yh:mean xh:mean time: mean
- long_name :
- Thickness of the surface Ocean Boundary Layer calculated by [CVMix] KPP
- time_avg_info :
- average_T1,average_T2,average_DT
- units :
- meter
Array Chunk Bytes 2.04 MiB 2.04 MiB Shape (534360,) (534360,) Dask graph 1 chunks in 3 graph layers Data type float32 numpy.ndarray - KPP_buoyFlux(time, zi)float32dask.array<chunksize=(8760, 37), meta=np.ndarray>
- cell_measures :
- area: areacello
- cell_methods :
- area:mean zi:point yh:mean xh:mean time: mean
- long_name :
- Surface (and penetrating) buoyancy flux, as used by [CVMix] KPP
- time_avg_info :
- average_T1,average_T2,average_DT
- units :
- m2/s3
Array Chunk Bytes 75.42 MiB 1.24 MiB Shape (534360, 37) (8760, 37) Dask graph 61 chunks in 5 graph layers Data type float32 numpy.ndarray - KPP_ustar(time)float32dask.array<chunksize=(534360,), meta=np.ndarray>
- cell_measures :
- area: areacello
- cell_methods :
- area:mean yh:mean xh:mean time: mean
- long_name :
- Friction velocity, u*, as used by [CVMix] KPP
- time_avg_info :
- average_T1,average_T2,average_DT
- units :
- m/s
Array Chunk Bytes 2.04 MiB 2.04 MiB Shape (534360,) (534360,) Dask graph 1 chunks in 3 graph layers Data type float32 numpy.ndarray - KS_extra(time, zi)float32dask.array<chunksize=(8760, 37), meta=np.ndarray>
- cell_measures :
- area: areacello
- cell_methods :
- area:mean zi:point yh:mean xh:mean time: mean
- long_name :
- Double-diffusive diffusivity for salinity
- time_avg_info :
- average_T1,average_T2,average_DT
- units :
- m2 s-1
Array Chunk Bytes 75.42 MiB 1.24 MiB Shape (534360, 37) (8760, 37) Dask graph 61 chunks in 5 graph layers Data type float32 numpy.ndarray - KT_extra(time, zi)float32dask.array<chunksize=(8760, 37), meta=np.ndarray>
- cell_measures :
- area: areacello
- cell_methods :
- area:mean zi:point yh:mean xh:mean time: mean
- long_name :
- Double-diffusive diffusivity for temperature
- time_avg_info :
- average_T1,average_T2,average_DT
- units :
- m2 s-1
Array Chunk Bytes 75.42 MiB 1.24 MiB Shape (534360, 37) (8760, 37) Dask graph 61 chunks in 5 graph layers Data type float32 numpy.ndarray - Kd_heat(time, zi)float32dask.array<chunksize=(8760, 37), meta=np.ndarray>
- cell_measures :
- area: areacello
- cell_methods :
- area:mean zi:point yh:mean xh:mean time: mean
- long_name :
- Total diapycnal diffusivity for heat at interfaces
- standard_name :
- ocean_vertical_heat_diffusivity
- time_avg_info :
- average_T1,average_T2,average_DT
- units :
- m2 s-1
Array Chunk Bytes 75.42 MiB 1.24 MiB Shape (534360, 37) (8760, 37) Dask graph 61 chunks in 5 graph layers Data type float32 numpy.ndarray - Kd_salt(time, zi)float32dask.array<chunksize=(8760, 37), meta=np.ndarray>
- cell_measures :
- area: areacello
- cell_methods :
- area:mean zi:point yh:mean xh:mean time: mean
- long_name :
- Total diapycnal diffusivity for salt at interfaces
- time_avg_info :
- average_T1,average_T2,average_DT
- units :
- m2 s-1
Array Chunk Bytes 75.42 MiB 1.24 MiB Shape (534360, 37) (8760, 37) Dask graph 61 chunks in 5 graph layers Data type float32 numpy.ndarray - Kv_u(time, zl)float32dask.array<chunksize=(8760, 37), meta=np.ndarray>
- cell_methods :
- zl:mean yh:mean xq:point time: mean
- interp_method :
- none
- long_name :
- Total vertical viscosity at u-points
- standard_name :
- ocean_vertical_x_viscosity
- time_avg_info :
- average_T1,average_T2,average_DT
- units :
- m2 s-1
Array Chunk Bytes 75.42 MiB 1.24 MiB Shape (534360, 37) (8760, 37) Dask graph 61 chunks in 5 graph layers Data type float32 numpy.ndarray - Kv_v(time, zl)float32dask.array<chunksize=(8760, 37), meta=np.ndarray>
- cell_methods :
- zl:mean yq:point xh:mean time: mean
- interp_method :
- none
- long_name :
- Total vertical viscosity at v-points
- standard_name :
- ocean_vertical_y_viscosity
- time_avg_info :
- average_T1,average_T2,average_DT
- units :
- m2 s-1
Array Chunk Bytes 75.42 MiB 1.24 MiB Shape (534360, 37) (8760, 37) Dask graph 61 chunks in 5 graph layers Data type float32 numpy.ndarray - N2(time, zi)float32dask.array<chunksize=(8760, 37), meta=np.ndarray>
- cell_measures :
- area: areacello
- cell_methods :
- area:mean zi:point yh:mean xh:mean time: mean
- long_name :
- Buoyancy frequency squared
- time_avg_info :
- average_T1,average_T2,average_DT
- units :
- s-2
Array Chunk Bytes 75.42 MiB 1.24 MiB Shape (534360, 37) (8760, 37) Dask graph 61 chunks in 5 graph layers Data type float32 numpy.ndarray - SSH(time)float32dask.array<chunksize=(534360,), meta=np.ndarray>
- cell_measures :
- area: areacello
- cell_methods :
- area:mean yh:mean xh:mean time: mean
- long_name :
- Sea Surface Height
- time_avg_info :
- average_T1,average_T2,average_DT
- units :
- m
Array Chunk Bytes 2.04 MiB 2.04 MiB Shape (534360,) (534360,) Dask graph 1 chunks in 3 graph layers Data type float32 numpy.ndarray - SW(time)float32dask.array<chunksize=(534360,), meta=np.ndarray>
- cell_measures :
- area: areacello
- cell_methods :
- area:mean yh:mean xh:mean time: mean
- long_name :
- Shortwave radiation flux into ocean
- standard_name :
- net_downward_shortwave_flux_at_sea_water_surface
- time_avg_info :
- average_T1,average_T2,average_DT
- units :
- W m-2
Array Chunk Bytes 2.04 MiB 2.04 MiB Shape (534360,) (534360,) Dask graph 1 chunks in 3 graph layers Data type float32 numpy.ndarray - SW_pen(time)float32dask.array<chunksize=(534360,), meta=np.ndarray>
- cell_measures :
- area: areacello
- cell_methods :
- area:mean yh:mean xh:mean time: mean
- long_name :
- Penetrating shortwave radiation flux into ocean
- time_avg_info :
- average_T1,average_T2,average_DT
- units :
- W m-2
Array Chunk Bytes 2.04 MiB 2.04 MiB Shape (534360,) (534360,) Dask graph 1 chunks in 3 graph layers Data type float32 numpy.ndarray - Tflx_dia_diff(time, zi)float32dask.array<chunksize=(8760, 37), meta=np.ndarray>
- cell_measures :
- area: areacello
- cell_methods :
- area:mean zi:point yh:mean xh:mean time: mean
- long_name :
- Diffusive diapycnal temperature flux across interfaces
- standard_name :
- ocean_vertical_diffusive_heat_flux
- time_avg_info :
- average_T1,average_T2,average_DT
- units :
- degC m s-1
Array Chunk Bytes 75.42 MiB 1.24 MiB Shape (534360, 37) (8760, 37) Dask graph 61 chunks in 5 graph layers Data type float32 numpy.ndarray - dens(time, zl)float32dask.array<chunksize=(8760, 37), meta=np.ndarray>
- cell_measures :
- volume: volcello area: areacello
- cell_methods :
- area:mean zl:mean yh:mean xh:mean time: mean
- long_name :
- Sea Water Salinity
- standard_name :
- sea_water_potential_density
- time_avg_info :
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- units :
- kg/m^3
Array Chunk Bytes 75.42 MiB 1.24 MiB Shape (534360, 37) (8760, 37) Dask graph 61 chunks in 5 graph layers Data type float32 numpy.ndarray - densT(time, zl)float32dask.array<chunksize=(8760, 37), meta=np.ndarray>
- cell_measures :
- volume: volcello area: areacello
- cell_methods :
- area:mean zl:mean yh:mean xh:mean time: mean
- long_name :
- Sea Water Potential Temperature
- standard_name :
- sea_water_potential_density
- time_avg_info :
- average_T1,average_T2,average_DT
- units :
- kg/m3
Array Chunk Bytes 75.42 MiB 1.24 MiB Shape (534360, 37) (8760, 37) Dask graph 61 chunks in 5 graph layers Data type float32 numpy.ndarray - h(time, zl)float32dask.array<chunksize=(8760, 37), meta=np.ndarray>
- cell_measures :
- volume: volcello area: areacello
- cell_methods :
- area:mean zl:sum yh:mean xh:mean time: mean
- long_name :
- Layer Thickness
- time_avg_info :
- average_T1,average_T2,average_DT
- units :
- m
Array Chunk Bytes 75.42 MiB 1.24 MiB Shape (534360, 37) (8760, 37) Dask graph 61 chunks in 5 graph layers Data type float32 numpy.ndarray - mlotst(time)float32dask.array<chunksize=(534360,), meta=np.ndarray>
- cell_measures :
- area: areacello
- cell_methods :
- area:mean yh:mean xh:mean time: mean
- long_name :
- Ocean Mixed Layer Thickness Defined by Sigma T
- standard_name :
- ocean_mixed_layer_thickness_defined_by_sigma_t
- time_avg_info :
- average_T1,average_T2,average_DT
- units :
- m
Array Chunk Bytes 2.04 MiB 2.04 MiB Shape (534360,) (534360,) Dask graph 1 chunks in 3 graph layers Data type float32 numpy.ndarray - net_heat_surface(time)float32dask.array<chunksize=(534360,), meta=np.ndarray>
- cell_measures :
- area: areacello
- cell_methods :
- area:mean yh:mean xh:mean time: mean
- long_name :
- Surface ocean heat flux from SW+LW+lat+sens+mass transfer+frazil+restore+seaice_melt_heat or flux adjustments
- standard_name :
- surface_downward_heat_flux_in_sea_water
- time_avg_info :
- average_T1,average_T2,average_DT
- units :
- W m-2
Array Chunk Bytes 2.04 MiB 2.04 MiB Shape (534360,) (534360,) Dask graph 1 chunks in 3 graph layers Data type float32 numpy.ndarray - ri_grad_shear(time, zi)float32dask.array<chunksize=(8760, 37), meta=np.ndarray>
- cell_measures :
- area: areacello
- cell_methods :
- area:mean zi:point yh:mean xh:mean time: mean
- long_name :
- Gradient Richarson number used by MOM_CVMix_shear module
- time_avg_info :
- average_T1,average_T2,average_DT
- units :
- nondim
Array Chunk Bytes 75.42 MiB 1.24 MiB Shape (534360, 37) (8760, 37) Dask graph 61 chunks in 5 graph layers Data type float32 numpy.ndarray - ri_grad_shear_orig(time, zi)float32dask.array<chunksize=(8760, 37), meta=np.ndarray>
- cell_measures :
- area: areacello
- cell_methods :
- area:mean zi:point yh:mean xh:mean time: mean
- long_name :
- Original gradient Richarson number, before smoothing was applied. This is part of the MOM_CVMix_shear module and only available
- time_avg_info :
- average_T1,average_T2,average_DT
- units :
- nondim
Array Chunk Bytes 75.42 MiB 1.24 MiB Shape (534360, 37) (8760, 37) Dask graph 61 chunks in 5 graph layers Data type float32 numpy.ndarray - so(time, zl)float32dask.array<chunksize=(8760, 37), meta=np.ndarray>
- cell_measures :
- volume: volcello area: areacello
- cell_methods :
- area:mean zl:mean yh:mean xh:mean time: mean
- long_name :
- Sea Water Salinity
- standard_name :
- sea_water_salinity
- time_avg_info :
- average_T1,average_T2,average_DT
- units :
- psu
Array Chunk Bytes 75.42 MiB 1.24 MiB Shape (534360, 37) (8760, 37) Dask graph 61 chunks in 5 graph layers Data type float32 numpy.ndarray - taux(time)float32dask.array<chunksize=(534360,), meta=np.ndarray>
- cell_methods :
- yh:mean xq:point time: mean
- interp_method :
- none
- long_name :
- Zonal surface stress from ocean interactions with atmos and ice
- standard_name :
- surface_downward_x_stress
- time_avg_info :
- average_T1,average_T2,average_DT
- units :
- Pa
Array Chunk Bytes 2.04 MiB 2.04 MiB Shape (534360,) (534360,) Dask graph 1 chunks in 3 graph layers Data type float32 numpy.ndarray - tauy(time)float32dask.array<chunksize=(534360,), meta=np.ndarray>
- cell_methods :
- yq:point xh:mean time: mean
- interp_method :
- none
- long_name :
- Meridional surface stress ocean interactions with atmos and ice
- standard_name :
- surface_downward_y_stress
- time_avg_info :
- average_T1,average_T2,average_DT
- units :
- Pa
Array Chunk Bytes 2.04 MiB 2.04 MiB Shape (534360,) (534360,) Dask graph 1 chunks in 3 graph layers Data type float32 numpy.ndarray - thetao(time, zl)float32dask.array<chunksize=(8760, 37), meta=np.ndarray>
- cell_measures :
- volume: volcello area: areacello
- cell_methods :
- area:mean zl:mean yh:mean xh:mean time: mean
- long_name :
- Sea Water Potential Temperature
- standard_name :
- sea_water_potential_temperature
- time_avg_info :
- average_T1,average_T2,average_DT
- units :
- degC
Array Chunk Bytes 75.42 MiB 1.24 MiB Shape (534360, 37) (8760, 37) Dask graph 61 chunks in 5 graph layers Data type float32 numpy.ndarray - uo(time, zl)float32dask.array<chunksize=(8760, 37), meta=np.ndarray>
- cell_methods :
- zl:mean yh:mean xq:point time: mean
- interp_method :
- none
- long_name :
- Sea Water X Velocity
- standard_name :
- sea_water_x_velocity
- time_avg_info :
- average_T1,average_T2,average_DT
- units :
- m s-1
Array Chunk Bytes 75.42 MiB 1.24 MiB Shape (534360, 37) (8760, 37) Dask graph 61 chunks in 5 graph layers Data type float32 numpy.ndarray - vo(time, zl)float32dask.array<chunksize=(8760, 37), meta=np.ndarray>
- cell_methods :
- zl:mean yq:point xh:mean time: mean
- interp_method :
- none
- long_name :
- Sea Water Y Velocity
- standard_name :
- sea_water_y_velocity
- time_avg_info :
- average_T1,average_T2,average_DT
- units :
- m s-1
Array Chunk Bytes 75.42 MiB 1.24 MiB Shape (534360, 37) (8760, 37) Dask graph 61 chunks in 5 graph layers Data type float32 numpy.ndarray - volcello(time, zl)float32dask.array<chunksize=(8760, 37), meta=np.ndarray>
- cell_methods :
- area:sum zl:sum yh:sum xh:sum time: mean
- long_name :
- Ocean grid-cell volume
- standard_name :
- ocean_volume
- time_avg_info :
- average_T1,average_T2,average_DT
- units :
- m3
Array Chunk Bytes 75.42 MiB 1.24 MiB Shape (534360, 37) (8760, 37) Dask graph 61 chunks in 5 graph layers Data type float32 numpy.ndarray - zos(time)float32dask.array<chunksize=(534360,), meta=np.ndarray>
- cell_measures :
- area: areacello
- cell_methods :
- area:mean yh:mean xh:mean time: mean
- long_name :
- Sea surface height above geoid
- standard_name :
- sea_surface_height_above_geoid
- time_avg_info :
- average_T1,average_T2,average_DT
- units :
- m
Array Chunk Bytes 2.04 MiB 2.04 MiB Shape (534360,) (534360,) Dask graph 1 chunks in 3 graph layers Data type float32 numpy.ndarray - α(time, zl)float32dask.array<chunksize=(8760, 37), meta=np.ndarray>
- standard_name :
- sea_water_thermal_expansion_coefficient
- units :
- C-1
Array Chunk Bytes 75.42 MiB 1.24 MiB Shape (534360, 37) (8760, 37) Dask graph 61 chunks in 5 graph layers Data type float32 numpy.ndarray - β(time, zl)float32dask.array<chunksize=(8760, 37), meta=np.ndarray>
- standard_name :
- sea_water_haline_contraction_coefficient
- units :
- kg/g
Array Chunk Bytes 75.42 MiB 1.24 MiB Shape (534360, 37) (8760, 37) Dask graph 61 chunks in 5 graph layers Data type float32 numpy.ndarray - Tz(time, zi)float32dask.array<chunksize=(8760, 36), meta=np.ndarray>
- long_name :
- $T_z$
- units :
- Cm$^{-1}$
Array Chunk Bytes 75.42 MiB 1.20 MiB Shape (534360, 37) (8760, 36) Dask graph 122 chunks in 23 graph layers Data type float32 numpy.ndarray - Sz(time, zi)float32dask.array<chunksize=(8760, 36), meta=np.ndarray>
- long_name :
- $S_z$
- units :
- m$^{-1}$
Array Chunk Bytes 75.42 MiB 1.20 MiB Shape (534360, 37) (8760, 36) Dask graph 122 chunks in 23 graph layers Data type float32 numpy.ndarray - N2T(time, zi)float32dask.array<chunksize=(8760, 36), meta=np.ndarray>
- long_name :
- $N_T^2$
- units :
- s$^{-2}$
Array Chunk Bytes 75.42 MiB 1.20 MiB Shape (534360, 37) (8760, 36) Dask graph 122 chunks in 27 graph layers Data type float32 numpy.ndarray - S2(time, zi)float32dask.array<chunksize=(8760, 36), meta=np.ndarray>
- long_name :
- $S^2$
- units :
- s$^{-2}$
Array Chunk Bytes 75.42 MiB 1.20 MiB Shape (534360, 37) (8760, 36) Dask graph 122 chunks in 50 graph layers Data type float32 numpy.ndarray - shred2(time, zi)float32dask.array<chunksize=(8760, 36), meta=np.ndarray>
- long_name :
- $Sh_{red}^2$
- units :
- $s^{-2}$
Array Chunk Bytes 75.42 MiB 1.20 MiB Shape (534360, 37) (8760, 36) Dask graph 122 chunks in 66 graph layers Data type float32 numpy.ndarray - Rig_T(time, zi)float32dask.array<chunksize=(8760, 36), meta=np.ndarray>
- long_name :
- $Ri^g_T$
Array Chunk Bytes 75.42 MiB 1.20 MiB Shape (534360, 37) (8760, 36) Dask graph 122 chunks in 65 graph layers Data type float32 numpy.ndarray - Rig(time, zi)float32dask.array<chunksize=(8760, 36), meta=np.ndarray>
- cell_measures :
- area: areacello
- cell_methods :
- area:mean zi:point yh:mean xh:mean time: mean
- long_name :
- $Ri^g$
- time_avg_info :
- average_T1,average_T2,average_DT
Array Chunk Bytes 75.42 MiB 1.20 MiB Shape (534360, 37) (8760, 36) Dask graph 122 chunks in 55 graph layers Data type float32 numpy.ndarray - tau(time)float32dask.array<chunksize=(534360,), meta=np.ndarray>
- cell_methods :
- yh:mean xq:point time: mean
- interp_method :
- none
- long_name :
- Zonal surface stress from ocean interactions with atmos and ice
- time_avg_info :
- average_T1,average_T2,average_DT
- units :
- Pa
Array Chunk Bytes 2.04 MiB 2.04 MiB Shape (534360,) (534360,) Dask graph 1 chunks in 9 graph layers Data type float32 numpy.ndarray - Jb(time, zi)float32dask.array<chunksize=(8760, 36), meta=np.ndarray>
- cell_measures :
- area: areacello
- cell_methods :
- area:mean zi:point yh:mean xh:mean time: mean
- long_name :
- Total diapycnal diffusivity for heat at interfaces
- standard_name :
- ocean_vertical_diffusive_buoyancy_flux
- time_avg_info :
- average_T1,average_T2,average_DT
- units :
- m2 s-1
Array Chunk Bytes 75.42 MiB 1.20 MiB Shape (534360, 37) (8760, 36) Dask graph 122 chunks in 62 graph layers Data type float32 numpy.ndarray - Jq(time, zi)float64dask.array<chunksize=(8760, 37), meta=np.ndarray>
- units :
- W/m^2
- long_name :
- $J_q^t$
Array Chunk Bytes 150.84 MiB 2.47 MiB Shape (534360, 37) (8760, 37) Dask graph 61 chunks in 6 graph layers Data type float64 numpy.ndarray - ν(time, zl)float32dask.array<chunksize=(8760, 37), meta=np.ndarray>
- cell_methods :
- zl:mean yh:mean xq:point time: mean
- interp_method :
- none
- long_name :
- Total vertical viscosity at u-points
- standard_name :
- ocean_vertical_momentum_diffusivity
- time_avg_info :
- average_T1,average_T2,average_DT
- units :
- m2 s-1
Array Chunk Bytes 75.42 MiB 1.24 MiB Shape (534360, 37) (8760, 37) Dask graph 61 chunks in 5 graph layers Data type float32 numpy.ndarray - shear_prod(time, zi)float32dask.array<chunksize=(8760, 36), meta=np.ndarray>
- long_name :
- $SP$
- units :
- W/kg
Array Chunk Bytes 75.42 MiB 1.20 MiB Shape (534360, 37) (8760, 36) Dask graph 122 chunks in 71 graph layers Data type float32 numpy.ndarray - eps(time, zi)float32dask.array<chunksize=(8760, 36), meta=np.ndarray>
- long_name :
- $ε$
- units :
- W/kg
Array Chunk Bytes 75.42 MiB 1.20 MiB Shape (534360, 37) (8760, 36) Dask graph 122 chunks in 121 graph layers Data type float32 numpy.ndarray - chi(time, zi)float32dask.array<chunksize=(8760, 36), meta=np.ndarray>
- long_name :
- $χ$
- units :
- C^2/s
Array Chunk Bytes 75.42 MiB 1.20 MiB Shape (534360, 37) (8760, 36) Dask graph 122 chunks in 30 graph layers Data type float32 numpy.ndarray - Rif(time, zi)float32dask.array<chunksize=(8760, 36), meta=np.ndarray>
- cell_measures :
- area: areacello
- cell_methods :
- area:mean zi:point yh:mean xh:mean time: mean
- long_name :
- Total diapycnal diffusivity for heat at interfaces
- standard_name :
- flux_richardson_number
- time_avg_info :
- average_T1,average_T2,average_DT
- units :
- m2 s-1
Array Chunk Bytes 75.42 MiB 1.20 MiB Shape (534360, 37) (8760, 36) Dask graph 122 chunks in 122 graph layers Data type float32 numpy.ndarray - sst(time)float32dask.array<chunksize=(8760,), meta=np.ndarray>
- cell_measures :
- volume: volcello area: areacello
- cell_methods :
- area:mean zl:mean yh:mean xh:mean time: mean
- long_name :
- $SST$
- standard_name :
- sea_surface_temperature
- time_avg_info :
- average_T1,average_T2,average_DT
- units :
- degC
Array Chunk Bytes 2.04 MiB 34.22 kiB Shape (534360,) (8760,) Dask graph 61 chunks in 5 graph layers Data type float32 numpy.ndarray
- title :
- KD=0, KV=0
<xarray.DatasetView> Dimensions: (time: 534360, zl: 37, zi: 37, nv: 2) Coordinates: (12/16) * nv (nv) float64 1.0 2.0 * time (time) datetime64[ns] 1958-01-01T00:30:00 ... 2018-... xh float64 -140.0 yh float64 0.0625 yq float64 -0.0625 * zi (zi) float64 -523.8 -481.0 -442.5 ... -5.0 -2.5 -0.0 ... ... oni (time) float32 1.66 1.66 1.66 1.66 ... 0.81 0.81 0.81 en_mask (time) bool True True True True ... True True True ln_mask (time) bool False False False ... False False False warm_mask (time) bool True True True True ... True True True cool_mask (time) bool False False False ... False False False enso_transition (time) <U12 'El-Nino warm' ... 'El-Nino warm' Data variables: (12/49) KPP_BulkRi (time, zl) float32 dask.array<chunksize=(8760, 37), meta=np.ndarray> KPP_NLtransport_heat (time, zi) float32 dask.array<chunksize=(8760, 37), meta=np.ndarray> KPP_OBLdepth (time) float32 dask.array<chunksize=(534360,), meta=np.ndarray> KPP_buoyFlux (time, zi) float32 dask.array<chunksize=(8760, 37), meta=np.ndarray> KPP_ustar (time) float32 dask.array<chunksize=(534360,), meta=np.ndarray> KS_extra (time, zi) float32 dask.array<chunksize=(8760, 37), meta=np.ndarray> ... ... ν (time, zl) float32 dask.array<chunksize=(8760, 37), meta=np.ndarray> shear_prod (time, zi) float32 dask.array<chunksize=(8760, 36), meta=np.ndarray> eps (time, zi) float32 dask.array<chunksize=(8760, 36), meta=np.ndarray> chi (time, zi) float32 dask.array<chunksize=(8760, 36), meta=np.ndarray> Rif (time, zi) float32 dask.array<chunksize=(8760, 36), meta=np.ndarray> sst (time) float32 dask.array<chunksize=(8760,), meta=np.ndarray> Attributes: title: KD=0, KV=0new_baseline.hb- time: 131400
- zl: 37
- zi: 37
- nv: 2
- nv(nv)float641.0 2.0
- long_name :
- vertex number
array([1., 2.])
- time(time)datetime64[ns]2003-01-01T00:30:00 ... 2017-12-...
array(['2003-01-01T00:30:00.000000000', '2003-01-01T01:30:00.000000000', '2003-01-01T02:30:00.000000000', ..., '2017-12-31T21:30:00.000000000', '2017-12-31T22:30:00.000000000', '2017-12-31T23:30:00.000000000'], dtype='datetime64[ns]') - xh()float64-140.0
- axis :
- X
- domain_decomposition :
- [220, 222, 220, 221]
- long_name :
- h point nominal longitude
- units :
- degrees_east
array(-140.)
- yh()float640.0625
- axis :
- Y
- domain_decomposition :
- [210, 258, 210, 221]
- long_name :
- h point nominal latitude
- units :
- degrees_north
array(0.06249997)
- yq()float64-0.0625
- axis :
- Y
- domain_decomposition :
- [209, 257, 209, 221]
- long_name :
- q point nominal latitude
- units :
- degrees_north
array(-0.06249997)
- zi(zi)float64-523.8 -481.0 -442.5 ... -2.5 -0.0
- axis :
- Z
- long_name :
- Interface pseudo-depth, -z*
- positive :
- up
- units :
- meter
array([-523.8 , -481.01, -442.51, -407.64, -375.88, -346.78, -319.99, -295.22, -272.22, -250.8 , -230.78, -212.02, -194.41, -177.85, -162.26, -147.57, -133.72, -120.66, -108.37, -96.83, -86.02, -75.94, -66.57, -57.91, -49.94, -42.66, -36.05, -30.1 , -24.81, -20.16, -16.15, -12.77, -10. , -7.5 , -5. , -2.5 , -0. ]) - zl(zl)float64-547.8 -502.4 ... -3.75 -1.25
- axis :
- Z
- long_name :
- Layer pseudo-depth, -z*
- positive :
- up
- units :
- meter
array([-547.75 , -502.405, -461.76 , -425.075, -391.76 , -361.33 , -333.385, -307.605, -283.72 , -261.51 , -240.79 , -221.4 , -203.215, -186.13 , -170.055, -154.915, -140.645, -127.19 , -114.515, -102.6 , -91.425, -80.98 , -71.255, -62.24 , -53.925, -46.3 , -39.355, -33.075, -27.455, -22.485, -18.155, -14.46 , -11.385, -8.75 , -6.25 , -3.75 , -1.25 ]) - eucmax(time)float64dask.array<chunksize=(8760,), meta=np.ndarray>
- units :
- m
- long_name :
- EUC maximum
- positive :
- up
Array Chunk Bytes 1.00 MiB 68.44 kiB Shape (131400,) (8760,) Dask graph 15 chunks in 21 graph layers Data type float64 numpy.ndarray - mldT(time)float64dask.array<chunksize=(8760,), meta=np.ndarray>
- long_name :
- MLD$_θ$
- units :
- m
- description :
- Interpolate θi to 1m grid. Search for max depth where |dθ| > 0.15
Array Chunk Bytes 1.00 MiB 68.44 kiB Shape (131400,) (8760,) Dask graph 15 chunks in 23 graph layers Data type float64 numpy.ndarray - dcl_mask(zi, time)booldask.array<chunksize=(37, 8760), meta=np.ndarray>
- description :
- True when 5m below mldT and above eucmax.
Array Chunk Bytes 4.64 MiB 316.52 kiB Shape (37, 131400) (37, 8760) Dask graph 15 chunks in 56 graph layers Data type bool numpy.ndarray - oni(time)float320.63 0.63 0.63 ... -0.87 -0.87
array([ 0.63, 0.63, 0.63, ..., -0.87, -0.87, -0.87], dtype=float32)
- en_mask(time)boolFalse False False ... False False
array([False, False, False, ..., False, False, False])
- ln_mask(time)boolFalse False False ... True True
array([False, False, False, ..., True, True, True])
- warm_mask(time)boolTrue True True ... True True True
array([ True, True, True, ..., True, True, True])
- cool_mask(time)boolFalse False False ... False False
array([False, False, False, ..., False, False, False])
- enso_transition(time)<U12'____________' ... 'La-Nina warm'
- description :
- Warner & Moum (2019) ENSO transition phase; El-Nino = ONI > 0.5 for at least 6 months; La-Nina = ONI < -0.5 for at least 6 months
array(['____________', '____________', '____________', ..., 'La-Nina warm', 'La-Nina warm', 'La-Nina warm'], dtype='<U12')
- KPP_BulkRi(time, zl)float32dask.array<chunksize=(8760, 37), meta=np.ndarray>
- cell_measures :
- volume: volcello area: areacello
- cell_methods :
- area:mean zl:mean yh:mean xh:mean time: mean
- long_name :
- Bulk Richardson number used to find the OBL depth used by [CVMix] KPP
- time_avg_info :
- average_T1,average_T2,average_DT
- units :
- nondim
Array Chunk Bytes 18.55 MiB 1.24 MiB Shape (131400, 37) (8760, 37) Dask graph 15 chunks in 5 graph layers Data type float32 numpy.ndarray - KPP_NLtransport_heat(time, zi)float32dask.array<chunksize=(8760, 37), meta=np.ndarray>
- cell_measures :
- area: areacello
- cell_methods :
- area:mean zi:point yh:mean xh:mean time: mean
- long_name :
- Non-local transport (Cs*G(sigma)) for heat, as calculated by [CVMix] KPP
- time_avg_info :
- average_T1,average_T2,average_DT
- units :
- nondim
Array Chunk Bytes 18.55 MiB 1.24 MiB Shape (131400, 37) (8760, 37) Dask graph 15 chunks in 5 graph layers Data type float32 numpy.ndarray - KPP_OBLdepth(time)float32dask.array<chunksize=(131400,), meta=np.ndarray>
- cell_measures :
- area: areacello
- cell_methods :
- area:mean yh:mean xh:mean time: mean
- long_name :
- Thickness of the surface Ocean Boundary Layer calculated by [CVMix] KPP
- time_avg_info :
- average_T1,average_T2,average_DT
- units :
- meter
Array Chunk Bytes 513.28 kiB 513.28 kiB Shape (131400,) (131400,) Dask graph 1 chunks in 3 graph layers Data type float32 numpy.ndarray - KPP_buoyFlux(time, zi)float32dask.array<chunksize=(8760, 37), meta=np.ndarray>
- cell_measures :
- area: areacello
- cell_methods :
- area:mean zi:point yh:mean xh:mean time: mean
- long_name :
- Surface (and penetrating) buoyancy flux, as used by [CVMix] KPP
- time_avg_info :
- average_T1,average_T2,average_DT
- units :
- m2/s3
Array Chunk Bytes 18.55 MiB 1.24 MiB Shape (131400, 37) (8760, 37) Dask graph 15 chunks in 5 graph layers Data type float32 numpy.ndarray - KPP_ustar(time)float32dask.array<chunksize=(131400,), meta=np.ndarray>
- cell_measures :
- area: areacello
- cell_methods :
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- units :
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Array Chunk Bytes 513.28 kiB 513.28 kiB Shape (131400,) (131400,) Dask graph 1 chunks in 3 graph layers Data type float32 numpy.ndarray - KS_extra(time, zi)float32dask.array<chunksize=(8760, 37), meta=np.ndarray>
- cell_measures :
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- time_avg_info :
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- units :
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Array Chunk Bytes 18.55 MiB 1.24 MiB Shape (131400, 37) (8760, 37) Dask graph 15 chunks in 5 graph layers Data type float32 numpy.ndarray - KT_extra(time, zi)float32dask.array<chunksize=(8760, 37), meta=np.ndarray>
- cell_measures :
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- units :
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Array Chunk Bytes 18.55 MiB 1.24 MiB Shape (131400, 37) (8760, 37) Dask graph 15 chunks in 5 graph layers Data type float32 numpy.ndarray - Kd_heat(time, zi)float32dask.array<chunksize=(8760, 37), meta=np.ndarray>
- cell_measures :
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- cell_methods :
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- standard_name :
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- time_avg_info :
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- units :
- m2 s-1
Array Chunk Bytes 18.55 MiB 1.24 MiB Shape (131400, 37) (8760, 37) Dask graph 15 chunks in 5 graph layers Data type float32 numpy.ndarray - Kd_salt(time, zi)float32dask.array<chunksize=(8760, 37), meta=np.ndarray>
- cell_measures :
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- units :
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Array Chunk Bytes 18.55 MiB 1.24 MiB Shape (131400, 37) (8760, 37) Dask graph 15 chunks in 5 graph layers Data type float32 numpy.ndarray - Kv_u(time, zl)float32dask.array<chunksize=(8760, 37), meta=np.ndarray>
- cell_methods :
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- interp_method :
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- long_name :
- Total vertical viscosity at u-points
- standard_name :
- ocean_vertical_x_viscosity
- time_avg_info :
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- units :
- m2 s-1
Array Chunk Bytes 18.55 MiB 1.24 MiB Shape (131400, 37) (8760, 37) Dask graph 15 chunks in 5 graph layers Data type float32 numpy.ndarray - Kv_v(time, zl)float32dask.array<chunksize=(8760, 37), meta=np.ndarray>
- cell_methods :
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- interp_method :
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- units :
- m2 s-1
Array Chunk Bytes 18.55 MiB 1.24 MiB Shape (131400, 37) (8760, 37) Dask graph 15 chunks in 5 graph layers Data type float32 numpy.ndarray - N2(time, zi)float32dask.array<chunksize=(8760, 37), meta=np.ndarray>
- cell_measures :
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- cell_methods :
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- units :
- s-2
Array Chunk Bytes 18.55 MiB 1.24 MiB Shape (131400, 37) (8760, 37) Dask graph 15 chunks in 5 graph layers Data type float32 numpy.ndarray - SSH(time)float32dask.array<chunksize=(131400,), meta=np.ndarray>
- cell_measures :
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- cell_methods :
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- units :
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Array Chunk Bytes 513.28 kiB 513.28 kiB Shape (131400,) (131400,) Dask graph 1 chunks in 3 graph layers Data type float32 numpy.ndarray - SW(time)float32dask.array<chunksize=(131400,), meta=np.ndarray>
- cell_measures :
- area: areacello
- cell_methods :
- area:mean yh:mean xh:mean time: mean
- long_name :
- Shortwave radiation flux into ocean
- standard_name :
- net_downward_shortwave_flux_at_sea_water_surface
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- average_T1,average_T2,average_DT
- units :
- W m-2
Array Chunk Bytes 513.28 kiB 513.28 kiB Shape (131400,) (131400,) Dask graph 1 chunks in 3 graph layers Data type float32 numpy.ndarray - SW_pen(time)float32dask.array<chunksize=(131400,), meta=np.ndarray>
- cell_measures :
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- cell_methods :
- area:mean yh:mean xh:mean time: mean
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- Penetrating shortwave radiation flux into ocean
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- units :
- W m-2
Array Chunk Bytes 513.28 kiB 513.28 kiB Shape (131400,) (131400,) Dask graph 1 chunks in 3 graph layers Data type float32 numpy.ndarray - Tflx_dia_diff(time, zi)float32dask.array<chunksize=(8760, 37), meta=np.ndarray>
- cell_measures :
- area: areacello
- cell_methods :
- area:mean zi:point yh:mean xh:mean time: mean
- long_name :
- Diffusive diapycnal temperature flux across interfaces
- standard_name :
- ocean_vertical_diffusive_heat_flux
- time_avg_info :
- average_T1,average_T2,average_DT
- units :
- degC m s-1
Array Chunk Bytes 18.55 MiB 1.24 MiB Shape (131400, 37) (8760, 37) Dask graph 15 chunks in 5 graph layers Data type float32 numpy.ndarray - dens(time, zl)float32dask.array<chunksize=(8760, 37), meta=np.ndarray>
- cell_measures :
- volume: volcello area: areacello
- cell_methods :
- area:mean zl:mean yh:mean xh:mean time: mean
- long_name :
- Sea Water Salinity
- standard_name :
- sea_water_potential_density
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- units :
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Array Chunk Bytes 18.55 MiB 1.24 MiB Shape (131400, 37) (8760, 37) Dask graph 15 chunks in 5 graph layers Data type float32 numpy.ndarray - densT(time, zl)float32dask.array<chunksize=(8760, 37), meta=np.ndarray>
- cell_measures :
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- Sea Water Potential Temperature
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- units :
- kg/m3
Array Chunk Bytes 18.55 MiB 1.24 MiB Shape (131400, 37) (8760, 37) Dask graph 15 chunks in 5 graph layers Data type float32 numpy.ndarray - h(time, zl)float32dask.array<chunksize=(8760, 37), meta=np.ndarray>
- cell_measures :
- volume: volcello area: areacello
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- Layer Thickness
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- units :
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Array Chunk Bytes 18.55 MiB 1.24 MiB Shape (131400, 37) (8760, 37) Dask graph 15 chunks in 5 graph layers Data type float32 numpy.ndarray - mlotst(time)float32dask.array<chunksize=(131400,), meta=np.ndarray>
- cell_measures :
- area: areacello
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- area:mean yh:mean xh:mean time: mean
- long_name :
- Ocean Mixed Layer Thickness Defined by Sigma T
- standard_name :
- ocean_mixed_layer_thickness_defined_by_sigma_t
- time_avg_info :
- average_T1,average_T2,average_DT
- units :
- m
Array Chunk Bytes 513.28 kiB 513.28 kiB Shape (131400,) (131400,) Dask graph 1 chunks in 3 graph layers Data type float32 numpy.ndarray - net_heat_surface(time)float32dask.array<chunksize=(131400,), meta=np.ndarray>
- cell_measures :
- area: areacello
- cell_methods :
- area:mean yh:mean xh:mean time: mean
- long_name :
- Surface ocean heat flux from SW+LW+lat+sens+mass transfer+frazil+restore+seaice_melt_heat or flux adjustments
- standard_name :
- surface_downward_heat_flux_in_sea_water
- time_avg_info :
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- units :
- W m-2
Array Chunk Bytes 513.28 kiB 513.28 kiB Shape (131400,) (131400,) Dask graph 1 chunks in 3 graph layers Data type float32 numpy.ndarray - ri_grad_shear(time, zi)float32dask.array<chunksize=(8760, 37), meta=np.ndarray>
- cell_measures :
- area: areacello
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- area:mean zi:point yh:mean xh:mean time: mean
- long_name :
- Gradient Richarson number used by MOM_CVMix_shear module
- time_avg_info :
- average_T1,average_T2,average_DT
- units :
- nondim
Array Chunk Bytes 18.55 MiB 1.24 MiB Shape (131400, 37) (8760, 37) Dask graph 15 chunks in 5 graph layers Data type float32 numpy.ndarray - ri_grad_shear_orig(time, zi)float32dask.array<chunksize=(8760, 37), meta=np.ndarray>
- cell_measures :
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- time_avg_info :
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- units :
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Array Chunk Bytes 18.55 MiB 1.24 MiB Shape (131400, 37) (8760, 37) Dask graph 15 chunks in 5 graph layers Data type float32 numpy.ndarray - so(time, zl)float32dask.array<chunksize=(8760, 37), meta=np.ndarray>
- cell_measures :
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- long_name :
- Sea Water Salinity
- standard_name :
- sea_water_salinity
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- units :
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Array Chunk Bytes 18.55 MiB 1.24 MiB Shape (131400, 37) (8760, 37) Dask graph 15 chunks in 5 graph layers Data type float32 numpy.ndarray - taux(time)float32dask.array<chunksize=(131400,), meta=np.ndarray>
- cell_methods :
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- standard_name :
- surface_downward_x_stress
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- units :
- Pa
Array Chunk Bytes 513.28 kiB 513.28 kiB Shape (131400,) (131400,) Dask graph 1 chunks in 3 graph layers Data type float32 numpy.ndarray - tauy(time)float32dask.array<chunksize=(131400,), meta=np.ndarray>
- cell_methods :
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- units :
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Array Chunk Bytes 513.28 kiB 513.28 kiB Shape (131400,) (131400,) Dask graph 1 chunks in 3 graph layers Data type float32 numpy.ndarray - thetao(time, zl)float32dask.array<chunksize=(8760, 37), meta=np.ndarray>
- cell_measures :
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- long_name :
- Sea Water Potential Temperature
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- units :
- degC
Array Chunk Bytes 18.55 MiB 1.24 MiB Shape (131400, 37) (8760, 37) Dask graph 15 chunks in 5 graph layers Data type float32 numpy.ndarray - uo(time, zl)float32dask.array<chunksize=(8760, 37), meta=np.ndarray>
- cell_methods :
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- time_avg_info :
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- units :
- m s-1
Array Chunk Bytes 18.55 MiB 1.24 MiB Shape (131400, 37) (8760, 37) Dask graph 15 chunks in 5 graph layers Data type float32 numpy.ndarray - vo(time, zl)float32dask.array<chunksize=(8760, 37), meta=np.ndarray>
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- units :
- m s-1
Array Chunk Bytes 18.55 MiB 1.24 MiB Shape (131400, 37) (8760, 37) Dask graph 15 chunks in 5 graph layers Data type float32 numpy.ndarray - volcello(time, zl)float32dask.array<chunksize=(8760, 37), meta=np.ndarray>
- cell_methods :
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- units :
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Array Chunk Bytes 18.55 MiB 1.24 MiB Shape (131400, 37) (8760, 37) Dask graph 15 chunks in 5 graph layers Data type float32 numpy.ndarray - zos(time)float32dask.array<chunksize=(131400,), meta=np.ndarray>
- cell_measures :
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- cell_methods :
- area:mean yh:mean xh:mean time: mean
- long_name :
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- time_avg_info :
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- units :
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Array Chunk Bytes 513.28 kiB 513.28 kiB Shape (131400,) (131400,) Dask graph 1 chunks in 3 graph layers Data type float32 numpy.ndarray - α(time, zl)float32dask.array<chunksize=(8760, 37), meta=np.ndarray>
- standard_name :
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- units :
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Array Chunk Bytes 18.55 MiB 1.24 MiB Shape (131400, 37) (8760, 37) Dask graph 15 chunks in 5 graph layers Data type float32 numpy.ndarray - β(time, zl)float32dask.array<chunksize=(8760, 37), meta=np.ndarray>
- standard_name :
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- units :
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Array Chunk Bytes 18.55 MiB 1.24 MiB Shape (131400, 37) (8760, 37) Dask graph 15 chunks in 5 graph layers Data type float32 numpy.ndarray - Tz(time, zi)float32dask.array<chunksize=(8760, 36), meta=np.ndarray>
- long_name :
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- units :
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Array Chunk Bytes 18.55 MiB 1.20 MiB Shape (131400, 37) (8760, 36) Dask graph 30 chunks in 23 graph layers Data type float32 numpy.ndarray - Sz(time, zi)float32dask.array<chunksize=(8760, 36), meta=np.ndarray>
- long_name :
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- units :
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Array Chunk Bytes 18.55 MiB 1.20 MiB Shape (131400, 37) (8760, 36) Dask graph 30 chunks in 23 graph layers Data type float32 numpy.ndarray - N2T(time, zi)float32dask.array<chunksize=(8760, 36), meta=np.ndarray>
- long_name :
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- units :
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Array Chunk Bytes 18.55 MiB 1.20 MiB Shape (131400, 37) (8760, 36) Dask graph 30 chunks in 27 graph layers Data type float32 numpy.ndarray - S2(time, zi)float32dask.array<chunksize=(8760, 36), meta=np.ndarray>
- long_name :
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- units :
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Array Chunk Bytes 18.55 MiB 1.20 MiB Shape (131400, 37) (8760, 36) Dask graph 30 chunks in 50 graph layers Data type float32 numpy.ndarray - shred2(time, zi)float32dask.array<chunksize=(8760, 36), meta=np.ndarray>
- long_name :
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- units :
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Array Chunk Bytes 18.55 MiB 1.20 MiB Shape (131400, 37) (8760, 36) Dask graph 30 chunks in 66 graph layers Data type float32 numpy.ndarray - Rig_T(time, zi)float32dask.array<chunksize=(8760, 36), meta=np.ndarray>
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Array Chunk Bytes 18.55 MiB 1.20 MiB Shape (131400, 37) (8760, 36) Dask graph 30 chunks in 65 graph layers Data type float32 numpy.ndarray - Rig(time, zi)float32dask.array<chunksize=(8760, 36), meta=np.ndarray>
- cell_measures :
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- long_name :
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Array Chunk Bytes 18.55 MiB 1.20 MiB Shape (131400, 37) (8760, 36) Dask graph 30 chunks in 55 graph layers Data type float32 numpy.ndarray - tau(time)float32dask.array<chunksize=(131400,), meta=np.ndarray>
- cell_methods :
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- units :
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Array Chunk Bytes 513.28 kiB 513.28 kiB Shape (131400,) (131400,) Dask graph 1 chunks in 9 graph layers Data type float32 numpy.ndarray - Jb(time, zi)float32dask.array<chunksize=(8760, 36), meta=np.ndarray>
- cell_measures :
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- standard_name :
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- units :
- m2 s-1
Array Chunk Bytes 18.55 MiB 1.20 MiB Shape (131400, 37) (8760, 36) Dask graph 30 chunks in 62 graph layers Data type float32 numpy.ndarray - Jq(time, zi)float64dask.array<chunksize=(8760, 37), meta=np.ndarray>
- units :
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- long_name :
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Array Chunk Bytes 37.09 MiB 2.47 MiB Shape (131400, 37) (8760, 37) Dask graph 15 chunks in 6 graph layers Data type float64 numpy.ndarray - ν(time, zl)float32dask.array<chunksize=(8760, 37), meta=np.ndarray>
- cell_methods :
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- interp_method :
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- long_name :
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- units :
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Array Chunk Bytes 18.55 MiB 1.24 MiB Shape (131400, 37) (8760, 37) Dask graph 15 chunks in 5 graph layers Data type float32 numpy.ndarray - shear_prod(time, zi)float32dask.array<chunksize=(8760, 36), meta=np.ndarray>
- long_name :
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- units :
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Array Chunk Bytes 18.55 MiB 1.20 MiB Shape (131400, 37) (8760, 36) Dask graph 30 chunks in 71 graph layers Data type float32 numpy.ndarray - eps(time, zi)float32dask.array<chunksize=(8760, 36), meta=np.ndarray>
- long_name :
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- units :
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Array Chunk Bytes 18.55 MiB 1.20 MiB Shape (131400, 37) (8760, 36) Dask graph 30 chunks in 121 graph layers Data type float32 numpy.ndarray - chi(time, zi)float32dask.array<chunksize=(8760, 36), meta=np.ndarray>
- long_name :
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- units :
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Array Chunk Bytes 18.55 MiB 1.20 MiB Shape (131400, 37) (8760, 36) Dask graph 30 chunks in 30 graph layers Data type float32 numpy.ndarray - Rif(time, zi)float32dask.array<chunksize=(8760, 36), meta=np.ndarray>
- cell_measures :
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- long_name :
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- units :
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Array Chunk Bytes 18.55 MiB 1.20 MiB Shape (131400, 37) (8760, 36) Dask graph 30 chunks in 122 graph layers Data type float32 numpy.ndarray - sst(time)float32dask.array<chunksize=(8760,), meta=np.ndarray>
- cell_measures :
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- cell_methods :
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- long_name :
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- standard_name :
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- time_avg_info :
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- units :
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Array Chunk Bytes 513.28 kiB 34.22 kiB Shape (131400,) (8760,) Dask graph 15 chunks in 5 graph layers Data type float32 numpy.ndarray
- title :
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<xarray.DatasetView> Dimensions: (time: 131400, zl: 37, zi: 37, nv: 2) Coordinates: (12/16) * nv (nv) float64 1.0 2.0 * time (time) datetime64[ns] 2003-01-01T00:30:00 ... 2017-... xh float64 -140.0 yh float64 0.0625 yq float64 -0.0625 * zi (zi) float64 -523.8 -481.0 -442.5 ... -5.0 -2.5 -0.0 ... ... oni (time) float32 0.63 0.63 0.63 ... -0.87 -0.87 -0.87 en_mask (time) bool False False False ... False False False ln_mask (time) bool False False False False ... True True True warm_mask (time) bool True True True True ... True True True cool_mask (time) bool False False False ... False False False enso_transition (time) <U12 '____________' ... 'La-Nina warm' Data variables: (12/49) KPP_BulkRi (time, zl) float32 dask.array<chunksize=(8760, 37), meta=np.ndarray> KPP_NLtransport_heat (time, zi) float32 dask.array<chunksize=(8760, 37), meta=np.ndarray> KPP_OBLdepth (time) float32 dask.array<chunksize=(131400,), meta=np.ndarray> KPP_buoyFlux (time, zi) float32 dask.array<chunksize=(8760, 37), meta=np.ndarray> KPP_ustar (time) float32 dask.array<chunksize=(131400,), meta=np.ndarray> KS_extra (time, zi) float32 dask.array<chunksize=(8760, 37), meta=np.ndarray> ... ... ν (time, zl) float32 dask.array<chunksize=(8760, 37), meta=np.ndarray> shear_prod (time, zi) float32 dask.array<chunksize=(8760, 36), meta=np.ndarray> eps (time, zi) float32 dask.array<chunksize=(8760, 36), meta=np.ndarray> chi (time, zi) float32 dask.array<chunksize=(8760, 36), meta=np.ndarray> Rif (time, zi) float32 dask.array<chunksize=(8760, 36), meta=np.ndarray> sst (time) float32 dask.array<chunksize=(8760,), meta=np.ndarray> Attributes: title: KPP ν0=2.5, Ric=0.2, Ri0=0.5new_baseline.kpp.lmd.004- time: 131400
- zl: 37
- zi: 37
- nv: 2
- nv(nv)float641.0 2.0
- long_name :
- vertex number
array([1., 2.])
- time(time)datetime64[ns]2003-01-01T00:30:00 ... 2017-12-...
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- axis :
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- positive :
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- units :
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- axis :
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Array Chunk Bytes 1.00 MiB 68.44 kiB Shape (131400,) (8760,) Dask graph 15 chunks in 21 graph layers Data type float64 numpy.ndarray - mldT(time)float64dask.array<chunksize=(8760,), meta=np.ndarray>
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- units :
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Array Chunk Bytes 1.00 MiB 68.44 kiB Shape (131400,) (8760,) Dask graph 15 chunks in 23 graph layers Data type float64 numpy.ndarray - dcl_mask(zi, time)booldask.array<chunksize=(37, 8760), meta=np.ndarray>
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- cool_mask(time)boolFalse False False ... False False
array([False, False, False, ..., False, False, False])
- enso_transition(time)<U12'____________' ... 'La-Nina warm'
- description :
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array(['____________', '____________', '____________', ..., 'La-Nina warm', 'La-Nina warm', 'La-Nina warm'], dtype='<U12')
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- cell_measures :
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Array Chunk Bytes 18.55 MiB 1.24 MiB Shape (131400, 37) (8760, 37) Dask graph 15 chunks in 5 graph layers Data type float32 numpy.ndarray - KPP_NLtransport_heat(time, zi)float32dask.array<chunksize=(8760, 37), meta=np.ndarray>
- cell_measures :
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Array Chunk Bytes 18.55 MiB 1.24 MiB Shape (131400, 37) (8760, 37) Dask graph 15 chunks in 5 graph layers Data type float32 numpy.ndarray - KPP_OBLdepth(time)float32dask.array<chunksize=(131400,), meta=np.ndarray>
- cell_measures :
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- long_name :
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- units :
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- cell_measures :
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- long_name :
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Array Chunk Bytes 18.55 MiB 1.24 MiB Shape (131400, 37) (8760, 37) Dask graph 15 chunks in 5 graph layers Data type float32 numpy.ndarray - KPP_ustar(time)float32dask.array<chunksize=(131400,), meta=np.ndarray>
- cell_measures :
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Array Chunk Bytes 513.28 kiB 513.28 kiB Shape (131400,) (131400,) Dask graph 1 chunks in 3 graph layers Data type float32 numpy.ndarray - KS_extra(time, zi)float32dask.array<chunksize=(8760, 37), meta=np.ndarray>
- cell_measures :
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Array Chunk Bytes 18.55 MiB 1.24 MiB Shape (131400, 37) (8760, 37) Dask graph 15 chunks in 5 graph layers Data type float32 numpy.ndarray - KT_extra(time, zi)float32dask.array<chunksize=(8760, 37), meta=np.ndarray>
- cell_measures :
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Array Chunk Bytes 18.55 MiB 1.24 MiB Shape (131400, 37) (8760, 37) Dask graph 15 chunks in 5 graph layers Data type float32 numpy.ndarray - Kd_heat(time, zi)float32dask.array<chunksize=(8760, 37), meta=np.ndarray>
- cell_measures :
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- long_name :
- Total diapycnal diffusivity for heat at interfaces
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- units :
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Array Chunk Bytes 18.55 MiB 1.24 MiB Shape (131400, 37) (8760, 37) Dask graph 15 chunks in 5 graph layers Data type float32 numpy.ndarray - Kd_salt(time, zi)float32dask.array<chunksize=(8760, 37), meta=np.ndarray>
- cell_measures :
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Array Chunk Bytes 18.55 MiB 1.24 MiB Shape (131400, 37) (8760, 37) Dask graph 15 chunks in 5 graph layers Data type float32 numpy.ndarray - Kv_u(time, zl)float32dask.array<chunksize=(8760, 37), meta=np.ndarray>
- cell_methods :
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- m2 s-1
Array Chunk Bytes 18.55 MiB 1.24 MiB Shape (131400, 37) (8760, 37) Dask graph 15 chunks in 5 graph layers Data type float32 numpy.ndarray - Kv_v(time, zl)float32dask.array<chunksize=(8760, 37), meta=np.ndarray>
- cell_methods :
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- interp_method :
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- units :
- m2 s-1
Array Chunk Bytes 18.55 MiB 1.24 MiB Shape (131400, 37) (8760, 37) Dask graph 15 chunks in 5 graph layers Data type float32 numpy.ndarray - N2(time, zi)float32dask.array<chunksize=(8760, 37), meta=np.ndarray>
- cell_measures :
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- long_name :
- Buoyancy frequency squared
- time_avg_info :
- average_T1,average_T2,average_DT
- units :
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Array Chunk Bytes 18.55 MiB 1.24 MiB Shape (131400, 37) (8760, 37) Dask graph 15 chunks in 5 graph layers Data type float32 numpy.ndarray - SSH(time)float32dask.array<chunksize=(131400,), meta=np.ndarray>
- cell_measures :
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- long_name :
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- units :
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Array Chunk Bytes 513.28 kiB 513.28 kiB Shape (131400,) (131400,) Dask graph 1 chunks in 3 graph layers Data type float32 numpy.ndarray - SW(time)float32dask.array<chunksize=(131400,), meta=np.ndarray>
- cell_measures :
- area: areacello
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- long_name :
- Shortwave radiation flux into ocean
- standard_name :
- net_downward_shortwave_flux_at_sea_water_surface
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- average_T1,average_T2,average_DT
- units :
- W m-2
Array Chunk Bytes 513.28 kiB 513.28 kiB Shape (131400,) (131400,) Dask graph 1 chunks in 3 graph layers Data type float32 numpy.ndarray - SW_pen(time)float32dask.array<chunksize=(131400,), meta=np.ndarray>
- cell_measures :
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- long_name :
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- average_T1,average_T2,average_DT
- units :
- W m-2
Array Chunk Bytes 513.28 kiB 513.28 kiB Shape (131400,) (131400,) Dask graph 1 chunks in 3 graph layers Data type float32 numpy.ndarray - Tflx_dia_diff(time, zi)float32dask.array<chunksize=(8760, 37), meta=np.ndarray>
- cell_measures :
- area: areacello
- cell_methods :
- area:mean zi:point yh:mean xh:mean time: mean
- long_name :
- Diffusive diapycnal temperature flux across interfaces
- standard_name :
- ocean_vertical_diffusive_heat_flux
- time_avg_info :
- average_T1,average_T2,average_DT
- units :
- degC m s-1
Array Chunk Bytes 18.55 MiB 1.24 MiB Shape (131400, 37) (8760, 37) Dask graph 15 chunks in 5 graph layers Data type float32 numpy.ndarray - dens(time, zl)float32dask.array<chunksize=(8760, 37), meta=np.ndarray>
- cell_measures :
- volume: volcello area: areacello
- cell_methods :
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- long_name :
- Sea Water Salinity
- standard_name :
- sea_water_potential_density
- time_avg_info :
- average_T1,average_T2,average_DT
- units :
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Array Chunk Bytes 18.55 MiB 1.24 MiB Shape (131400, 37) (8760, 37) Dask graph 15 chunks in 5 graph layers Data type float32 numpy.ndarray - densT(time, zl)float32dask.array<chunksize=(8760, 37), meta=np.ndarray>
- cell_measures :
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- Sea Water Potential Temperature
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- units :
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Array Chunk Bytes 18.55 MiB 1.24 MiB Shape (131400, 37) (8760, 37) Dask graph 15 chunks in 5 graph layers Data type float32 numpy.ndarray - h(time, zl)float32dask.array<chunksize=(8760, 37), meta=np.ndarray>
- cell_measures :
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- cell_methods :
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- long_name :
- Layer Thickness
- time_avg_info :
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- units :
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Array Chunk Bytes 18.55 MiB 1.24 MiB Shape (131400, 37) (8760, 37) Dask graph 15 chunks in 5 graph layers Data type float32 numpy.ndarray - mlotst(time)float32dask.array<chunksize=(131400,), meta=np.ndarray>
- cell_measures :
- area: areacello
- cell_methods :
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- long_name :
- Ocean Mixed Layer Thickness Defined by Sigma T
- standard_name :
- ocean_mixed_layer_thickness_defined_by_sigma_t
- time_avg_info :
- average_T1,average_T2,average_DT
- units :
- m
Array Chunk Bytes 513.28 kiB 513.28 kiB Shape (131400,) (131400,) Dask graph 1 chunks in 3 graph layers Data type float32 numpy.ndarray - net_heat_surface(time)float32dask.array<chunksize=(131400,), meta=np.ndarray>
- cell_measures :
- area: areacello
- cell_methods :
- area:mean yh:mean xh:mean time: mean
- long_name :
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- standard_name :
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- time_avg_info :
- average_T1,average_T2,average_DT
- units :
- W m-2
Array Chunk Bytes 513.28 kiB 513.28 kiB Shape (131400,) (131400,) Dask graph 1 chunks in 3 graph layers Data type float32 numpy.ndarray - ri_grad_shear(time, zi)float32dask.array<chunksize=(8760, 37), meta=np.ndarray>
- cell_measures :
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- long_name :
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- time_avg_info :
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- units :
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Array Chunk Bytes 18.55 MiB 1.24 MiB Shape (131400, 37) (8760, 37) Dask graph 15 chunks in 5 graph layers Data type float32 numpy.ndarray - ri_grad_shear_orig(time, zi)float32dask.array<chunksize=(8760, 37), meta=np.ndarray>
- cell_measures :
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- time_avg_info :
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- units :
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Array Chunk Bytes 18.55 MiB 1.24 MiB Shape (131400, 37) (8760, 37) Dask graph 15 chunks in 5 graph layers Data type float32 numpy.ndarray - so(time, zl)float32dask.array<chunksize=(8760, 37), meta=np.ndarray>
- cell_measures :
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- long_name :
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- time_avg_info :
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- units :
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Array Chunk Bytes 18.55 MiB 1.24 MiB Shape (131400, 37) (8760, 37) Dask graph 15 chunks in 5 graph layers Data type float32 numpy.ndarray - taux(time)float32dask.array<chunksize=(131400,), meta=np.ndarray>
- cell_methods :
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- interp_method :
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- long_name :
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- standard_name :
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- units :
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Array Chunk Bytes 513.28 kiB 513.28 kiB Shape (131400,) (131400,) Dask graph 1 chunks in 3 graph layers Data type float32 numpy.ndarray - tauy(time)float32dask.array<chunksize=(131400,), meta=np.ndarray>
- cell_methods :
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- interp_method :
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- long_name :
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- units :
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Array Chunk Bytes 513.28 kiB 513.28 kiB Shape (131400,) (131400,) Dask graph 1 chunks in 3 graph layers Data type float32 numpy.ndarray - thetao(time, zl)float32dask.array<chunksize=(8760, 37), meta=np.ndarray>
- cell_measures :
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- long_name :
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- units :
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Array Chunk Bytes 18.55 MiB 1.24 MiB Shape (131400, 37) (8760, 37) Dask graph 15 chunks in 5 graph layers Data type float32 numpy.ndarray - uo(time, zl)float32dask.array<chunksize=(8760, 37), meta=np.ndarray>
- cell_methods :
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- long_name :
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- units :
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Array Chunk Bytes 18.55 MiB 1.24 MiB Shape (131400, 37) (8760, 37) Dask graph 15 chunks in 5 graph layers Data type float32 numpy.ndarray - vo(time, zl)float32dask.array<chunksize=(8760, 37), meta=np.ndarray>
- cell_methods :
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- interp_method :
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- long_name :
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- units :
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Array Chunk Bytes 18.55 MiB 1.24 MiB Shape (131400, 37) (8760, 37) Dask graph 15 chunks in 5 graph layers Data type float32 numpy.ndarray - volcello(time, zl)float32dask.array<chunksize=(8760, 37), meta=np.ndarray>
- cell_methods :
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- long_name :
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- units :
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Array Chunk Bytes 18.55 MiB 1.24 MiB Shape (131400, 37) (8760, 37) Dask graph 15 chunks in 5 graph layers Data type float32 numpy.ndarray - zos(time)float32dask.array<chunksize=(131400,), meta=np.ndarray>
- cell_measures :
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- long_name :
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- units :
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Array Chunk Bytes 513.28 kiB 513.28 kiB Shape (131400,) (131400,) Dask graph 1 chunks in 3 graph layers Data type float32 numpy.ndarray - α(time, zl)float32dask.array<chunksize=(8760, 37), meta=np.ndarray>
- standard_name :
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Array Chunk Bytes 18.55 MiB 1.24 MiB Shape (131400, 37) (8760, 37) Dask graph 15 chunks in 5 graph layers Data type float32 numpy.ndarray - β(time, zl)float32dask.array<chunksize=(8760, 37), meta=np.ndarray>
- standard_name :
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Array Chunk Bytes 18.55 MiB 1.24 MiB Shape (131400, 37) (8760, 37) Dask graph 15 chunks in 5 graph layers Data type float32 numpy.ndarray - Tz(time, zi)float32dask.array<chunksize=(8760, 36), meta=np.ndarray>
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- long_name :
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Array Chunk Bytes 18.55 MiB 1.20 MiB Shape (131400, 37) (8760, 36) Dask graph 30 chunks in 23 graph layers Data type float32 numpy.ndarray - N2T(time, zi)float32dask.array<chunksize=(8760, 36), meta=np.ndarray>
- long_name :
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- units :
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- long_name :
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- units :
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Array Chunk Bytes 18.55 MiB 1.20 MiB Shape (131400, 37) (8760, 36) Dask graph 30 chunks in 50 graph layers Data type float32 numpy.ndarray - shred2(time, zi)float32dask.array<chunksize=(8760, 36), meta=np.ndarray>
- long_name :
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Array Chunk Bytes 18.55 MiB 1.20 MiB Shape (131400, 37) (8760, 36) Dask graph 30 chunks in 66 graph layers Data type float32 numpy.ndarray - Rig_T(time, zi)float32dask.array<chunksize=(8760, 36), meta=np.ndarray>
- long_name :
- $Ri^g_T$
Array Chunk Bytes 18.55 MiB 1.20 MiB Shape (131400, 37) (8760, 36) Dask graph 30 chunks in 65 graph layers Data type float32 numpy.ndarray - Rig(time, zi)float32dask.array<chunksize=(8760, 36), meta=np.ndarray>
- cell_measures :
- area: areacello
- cell_methods :
- area:mean zi:point yh:mean xh:mean time: mean
- long_name :
- $Ri^g$
- time_avg_info :
- average_T1,average_T2,average_DT
Array Chunk Bytes 18.55 MiB 1.20 MiB Shape (131400, 37) (8760, 36) Dask graph 30 chunks in 55 graph layers Data type float32 numpy.ndarray - tau(time)float32dask.array<chunksize=(131400,), meta=np.ndarray>
- cell_methods :
- yh:mean xq:point time: mean
- interp_method :
- none
- long_name :
- Zonal surface stress from ocean interactions with atmos and ice
- time_avg_info :
- average_T1,average_T2,average_DT
- units :
- Pa
Array Chunk Bytes 513.28 kiB 513.28 kiB Shape (131400,) (131400,) Dask graph 1 chunks in 9 graph layers Data type float32 numpy.ndarray - Jb(time, zi)float32dask.array<chunksize=(8760, 36), meta=np.ndarray>
- cell_measures :
- area: areacello
- cell_methods :
- area:mean zi:point yh:mean xh:mean time: mean
- long_name :
- Total diapycnal diffusivity for heat at interfaces
- standard_name :
- ocean_vertical_diffusive_buoyancy_flux
- time_avg_info :
- average_T1,average_T2,average_DT
- units :
- m2 s-1
Array Chunk Bytes 18.55 MiB 1.20 MiB Shape (131400, 37) (8760, 36) Dask graph 30 chunks in 62 graph layers Data type float32 numpy.ndarray - Jq(time, zi)float64dask.array<chunksize=(8760, 37), meta=np.ndarray>
- units :
- W/m^2
- long_name :
- $J_q^t$
Array Chunk Bytes 37.09 MiB 2.47 MiB Shape (131400, 37) (8760, 37) Dask graph 15 chunks in 6 graph layers Data type float64 numpy.ndarray - ν(time, zl)float32dask.array<chunksize=(8760, 37), meta=np.ndarray>
- cell_methods :
- zl:mean yh:mean xq:point time: mean
- interp_method :
- none
- long_name :
- Total vertical viscosity at u-points
- standard_name :
- ocean_vertical_momentum_diffusivity
- time_avg_info :
- average_T1,average_T2,average_DT
- units :
- m2 s-1
Array Chunk Bytes 18.55 MiB 1.24 MiB Shape (131400, 37) (8760, 37) Dask graph 15 chunks in 5 graph layers Data type float32 numpy.ndarray - shear_prod(time, zi)float32dask.array<chunksize=(8760, 36), meta=np.ndarray>
- long_name :
- $SP$
- units :
- W/kg
Array Chunk Bytes 18.55 MiB 1.20 MiB Shape (131400, 37) (8760, 36) Dask graph 30 chunks in 71 graph layers Data type float32 numpy.ndarray - eps(time, zi)float32dask.array<chunksize=(8760, 36), meta=np.ndarray>
- long_name :
- $ε$
- units :
- W/kg
Array Chunk Bytes 18.55 MiB 1.20 MiB Shape (131400, 37) (8760, 36) Dask graph 30 chunks in 121 graph layers Data type float32 numpy.ndarray - chi(time, zi)float32dask.array<chunksize=(8760, 36), meta=np.ndarray>
- long_name :
- $χ$
- units :
- C^2/s
Array Chunk Bytes 18.55 MiB 1.20 MiB Shape (131400, 37) (8760, 36) Dask graph 30 chunks in 30 graph layers Data type float32 numpy.ndarray - Rif(time, zi)float32dask.array<chunksize=(8760, 36), meta=np.ndarray>
- cell_measures :
- area: areacello
- cell_methods :
- area:mean zi:point yh:mean xh:mean time: mean
- long_name :
- Total diapycnal diffusivity for heat at interfaces
- standard_name :
- flux_richardson_number
- time_avg_info :
- average_T1,average_T2,average_DT
- units :
- m2 s-1
Array Chunk Bytes 18.55 MiB 1.20 MiB Shape (131400, 37) (8760, 36) Dask graph 30 chunks in 122 graph layers Data type float32 numpy.ndarray - sst(time)float32dask.array<chunksize=(8760,), meta=np.ndarray>
- cell_measures :
- volume: volcello area: areacello
- cell_methods :
- area:mean zl:mean yh:mean xh:mean time: mean
- long_name :
- $SST$
- standard_name :
- sea_surface_temperature
- time_avg_info :
- average_T1,average_T2,average_DT
- units :
- degC
Array Chunk Bytes 513.28 kiB 34.22 kiB Shape (131400,) (8760,) Dask graph 15 chunks in 5 graph layers Data type float32 numpy.ndarray
- title :
- KPP ν0=2.5, Ri0=0.5
<xarray.DatasetView> Dimensions: (time: 131400, zl: 37, zi: 37, nv: 2) Coordinates: (12/16) * nv (nv) float64 1.0 2.0 * time (time) datetime64[ns] 2003-01-01T00:30:00 ... 2017-... xh float64 -140.0 yh float64 0.0625 yq float64 -0.0625 * zi (zi) float64 -523.8 -481.0 -442.5 ... -5.0 -2.5 -0.0 ... ... oni (time) float32 0.63 0.63 0.63 ... -0.87 -0.87 -0.87 en_mask (time) bool False False False ... False False False ln_mask (time) bool False False False False ... True True True warm_mask (time) bool True True True True ... True True True cool_mask (time) bool False False False ... False False False enso_transition (time) <U12 '____________' ... 'La-Nina warm' Data variables: (12/49) KPP_BulkRi (time, zl) float32 dask.array<chunksize=(8760, 37), meta=np.ndarray> KPP_NLtransport_heat (time, zi) float32 dask.array<chunksize=(8760, 37), meta=np.ndarray> KPP_OBLdepth (time) float32 dask.array<chunksize=(131400,), meta=np.ndarray> KPP_buoyFlux (time, zi) float32 dask.array<chunksize=(8760, 37), meta=np.ndarray> KPP_ustar (time) float32 dask.array<chunksize=(131400,), meta=np.ndarray> KS_extra (time, zi) float32 dask.array<chunksize=(8760, 37), meta=np.ndarray> ... ... ν (time, zl) float32 dask.array<chunksize=(8760, 37), meta=np.ndarray> shear_prod (time, zi) float32 dask.array<chunksize=(8760, 36), meta=np.ndarray> eps (time, zi) float32 dask.array<chunksize=(8760, 36), meta=np.ndarray> chi (time, zi) float32 dask.array<chunksize=(8760, 36), meta=np.ndarray> Rif (time, zi) float32 dask.array<chunksize=(8760, 36), meta=np.ndarray> sst (time) float32 dask.array<chunksize=(8760,), meta=np.ndarray> Attributes: title: KPP ν0=2.5, Ri0=0.5new_baseline.kpp.lmd.005
Reprocess 004, 005 TO DELETE#
from pump import mixpods
from mom6_tools.kerchunk import (
combine_stream_jsons_as_groups,
generate_references_for_stream,
)
for casename in [
"gmom.e23.GJRAv3.TL319_t061_zstar_N65.new_baseline.kpp.lmd.004.mixpods",
"gmom.e23.GJRAv3.TL319_t061_zstar_N65.new_baseline.kpp.lmd.005.mixpods",
]:
caseroot = f"{mixpods.ROOT}/cesm/{casename}"
print(caseroot)
for stream in ["h", "hm", "hm.wci", "sfc"]:
generate_references_for_stream(
caseroot=caseroot,
stream=stream,
missing_stream="warn",
existing_output="overwrite",
)
combine_stream_jsons_as_groups(caseroot=caseroot)
import pump
pump.catalog.build_mom6_catalog()
/glade/campaign/cgd/oce/projects/pump//cesm/gmom.e23.GJRAv3.TL319_t061_zstar_N65.new_baseline.kpp.lmd.004.mixpods
/glade/u/home/dcherian/python/mom6-tools/mom6_tools/kerchunk.py:104: RuntimeWarning: No files found for caseroot: /glade/campaign/cgd/oce/projects/pump//cesm/gmom.e23.GJRAv3.TL319_t061_zstar_N65.new_baseline.kpp.lmd.004.mixpods, stream: hm.wci
warnings.warn(f"No files found for caseroot: {caseroot}, stream: {stream}", RuntimeWarning)
/glade/campaign/cgd/oce/projects/pump//cesm/gmom.e23.GJRAv3.TL319_t061_zstar_N65.new_baseline.kpp.lmd.005.mixpods
/glade/u/home/dcherian/python/mom6-tools/mom6_tools/kerchunk.py:104: RuntimeWarning: No files found for caseroot: /glade/campaign/cgd/oce/projects/pump//cesm/gmom.e23.GJRAv3.TL319_t061_zstar_N65.new_baseline.kpp.lmd.005.mixpods, stream: hm.wci
warnings.warn(f"No files found for caseroot: {caseroot}, stream: {stream}", RuntimeWarning)
Successfully wrote ESM catalog json file to: file:///glade/campaign/cgd/oce/projects/pump/catalogs/pump-mom6-catalog.json
for casename in [
"gmom.e23.GJRAv3.TL319_t061_zstar_N65.new_baseline.kpp.lmd.004.mixpods",
"gmom.e23.GJRAv3.TL319_t061_zstar_N65.new_baseline.kpp.lmd.005.mixpods",
]:
mixpods.mom6_sections_to_zarr(casename)
100%|██████████| 33/33 [00:18<00:00, 1.75it/s]
100%|██████████| 33/33 [00:19<00:00, 1.68it/s]
Add TAO#
tao_gridded = mixpods.load_tao()
tree["TAO"] = DataTree(tao_gridded)
tree = tree.dc.reorder_nodes(["TAO", ...])
/glade/u/home/dcherian/miniconda3/envs/pump/lib/python3.10/site-packages/xarray/core/dataset.py:265: UserWarning: The specified chunks separate the stored chunks along dimension "depth" starting at index 42. This could degrade performance. Instead, consider rechunking after loading.
warnings.warn(
/glade/u/home/dcherian/miniconda3/envs/pump/lib/python3.10/site-packages/xarray/core/dataset.py:265: UserWarning: The specified chunks separate the stored chunks along dimension "time" starting at index 199726. This could degrade performance. Instead, consider rechunking after loading.
warnings.warn(
/glade/u/home/dcherian/miniconda3/envs/pump/lib/python3.10/site-packages/xarray/core/dataset.py:265: UserWarning: The specified chunks separate the stored chunks along dimension "longitude" starting at index 2. This could degrade performance. Instead, consider rechunking after loading.
warnings.warn(
Add LES, microstructure#
Check SST time series#
sst = (
xr.Dataset(
{
casename: node["sst"].reset_coords(drop=True)
for casename, node in tree.children.items()
}
)
.to_array("case")
.load()
)
sst.rename("sst").resample(time="D").mean().hvplot.line(muted_alpha=0, by="case")
Post-process catalog subset#
Slice#
tree = tree.sel(time=slice("2003", "2017"))
tree
<xarray.DatasetView>
Dimensions: ()
Data variables:
*empty*- time: 131496
- depth: 61
- depthchi: 6
- deepest(time)float64dask.array<chunksize=(131496,), meta=np.ndarray>
- description :
- Deepest depth with a valid observation
- units :
- m
Array Chunk Bytes 1.00 MiB 1.00 MiB Shape (131496,) (131496,) Dask graph 1 chunks in 4 graph layers Data type float64 numpy.ndarray - depth(depth)float64-300.0 -295.0 -290.0 ... -5.0 0.0
- axis :
- Z
- positive :
- up
- units :
- m
array([-300., -295., -290., -285., -280., -275., -270., -265., -260., -255., -250., -245., -240., -235., -230., -225., -220., -215., -210., -205., -200., -195., -190., -185., -180., -175., -170., -165., -160., -155., -150., -145., -140., -135., -130., -125., -120., -115., -110., -105., -100., -95., -90., -85., -80., -75., -70., -65., -60., -55., -50., -45., -40., -35., -30., -25., -20., -15., -10., -5., 0.]) - eucmax(time)float64dask.array<chunksize=(71762,), meta=np.ndarray>
- units :
- m
- long_name :
- EUC maximum
- positive :
- up
Array Chunk Bytes 1.00 MiB 560.64 kiB Shape (131496,) (71762,) Dask graph 2 chunks in 18 graph layers Data type float64 numpy.ndarray - latitude()float320.0
array(0., dtype=float32)
- longitude()float32-140.0
array(-140., dtype=float32)
- mld(time)float64dask.array<chunksize=(131496,), meta=np.ndarray>
- long_name :
- $z_{MLD}$
- units :
- m
Array Chunk Bytes 1.00 MiB 1.00 MiB Shape (131496,) (131496,) Dask graph 1 chunks in 4 graph layers Data type float64 numpy.ndarray - mldT(time)float64dask.array<chunksize=(71762,), meta=np.ndarray>
- long_name :
- MLD$_θ$
- units :
- m
- description :
- Interpolate θi to 1m grid. Search for max depth where |dθ| > 0.15
Array Chunk Bytes 1.00 MiB 560.64 kiB Shape (131496,) (71762,) Dask graph 2 chunks in 21 graph layers Data type float64 numpy.ndarray - reference_pressure()int640
array(0)
- shallowest(time)float64dask.array<chunksize=(131496,), meta=np.ndarray>
Array Chunk Bytes 1.00 MiB 1.00 MiB Shape (131496,) (131496,) Dask graph 1 chunks in 4 graph layers Data type float64 numpy.ndarray - time(time)datetime64[ns]2003-01-01 ... 2017-12-31T23:00:00
array(['2003-01-01T00:00:00.000000000', '2003-01-01T01:00:00.000000000', '2003-01-01T02:00:00.000000000', ..., '2017-12-31T21:00:00.000000000', '2017-12-31T22:00:00.000000000', '2017-12-31T23:00:00.000000000'], dtype='datetime64[ns]') - zeuc(depth, time)float64dask.array<chunksize=(42, 71488), meta=np.ndarray>
Array Chunk Bytes 61.20 MiB 22.91 MiB Shape (61, 131496) (42, 71488) Dask graph 4 chunks in 4 graph layers Data type float64 numpy.ndarray - depthchi(depthchi)float64-89.0 -69.0 -59.0 -49.0 -39.0 -29.0
- axis :
- Z
- positive :
- up
- units :
- m
array([-89., -69., -59., -49., -39., -29.])
- dcl_mask(depth, time)booldask.array<chunksize=(61, 71762), meta=np.ndarray>
- description :
- True when 5m below mldT and above eucmax.
Array Chunk Bytes 7.65 MiB 4.17 MiB Shape (61, 131496) (61, 71762) Dask graph 2 chunks in 46 graph layers Data type bool numpy.ndarray - oni(time)float320.92 0.92 0.92 ... -0.87 -0.87
array([ 0.92, 0.92, 0.92, ..., -0.87, -0.87, -0.87], dtype=float32)
- en_mask(time)boolTrue True True ... False False
array([ True, True, True, ..., False, False, False])
- ln_mask(time)boolFalse False False ... True True
array([False, False, False, ..., True, True, True])
- warm_mask(time)boolFalse False False ... True True
array([False, False, False, ..., True, True, True])
- cool_mask(time)boolTrue True True ... False False
array([ True, True, True, ..., False, False, False])
- enso_transition(time)<U12'El-Nino cool' ... 'La-Nina warm'
- description :
- Warner & Moum (2019) ENSO transition phase; El-Nino = ONI > 0.5 for at least 6 months; La-Nina = ONI < -0.5 for at least 6 months
array(['El-Nino cool', 'El-Nino cool', 'El-Nino cool', ..., 'La-Nina warm', 'La-Nina warm', 'La-Nina warm'], dtype='<U12')
- N2(time, depth)float64dask.array<chunksize=(71762, 61), meta=np.ndarray>
- long_name :
- $N^2$
Array Chunk Bytes 61.20 MiB 33.40 MiB Shape (131496, 61) (71762, 61) Dask graph 2 chunks in 4 graph layers Data type float64 numpy.ndarray - N2T(time, depth)float64dask.array<chunksize=(71762, 61), meta=np.ndarray>
- long_name :
- $N^2_T$
Array Chunk Bytes 61.20 MiB 33.40 MiB Shape (131496, 61) (71762, 61) Dask graph 2 chunks in 4 graph layers Data type float64 numpy.ndarray - Ri(time, depth)float64dask.array<chunksize=(71762, 61), meta=np.ndarray>
- long_name :
- $Ri_g$
Array Chunk Bytes 61.20 MiB 33.40 MiB Shape (131496, 61) (71762, 61) Dask graph 2 chunks in 4 graph layers Data type float64 numpy.ndarray - Rig_T(time, depth)float64dask.array<chunksize=(71762, 61), meta=np.ndarray>
- long_name :
- $Ri^g_T$
Array Chunk Bytes 61.20 MiB 33.40 MiB Shape (131496, 61) (71762, 61) Dask graph 2 chunks in 8 graph layers Data type float64 numpy.ndarray - S(time, depth)float64dask.array<chunksize=(71762, 61), meta=np.ndarray>
- FORTRAN_format :
- epic_code :
- 41
- generic_name :
- sal
- long_name :
- SALINITY (PSU)
- name :
- S
- standard_name :
- sea_water_salinity
- units :
- PSU
Array Chunk Bytes 61.20 MiB 33.40 MiB Shape (131496, 61) (71762, 61) Dask graph 2 chunks in 4 graph layers Data type float64 numpy.ndarray - S2(time, depth)float32dask.array<chunksize=(71762, 61), meta=np.ndarray>
- long_name :
- $S^2$
Array Chunk Bytes 30.60 MiB 16.70 MiB Shape (131496, 61) (71762, 61) Dask graph 2 chunks in 4 graph layers Data type float32 numpy.ndarray - T(time, depth)float64dask.array<chunksize=(71762, 61), meta=np.ndarray>
- FORTRAN_format :
- f10.2
- epic_code :
- 20
- generic_name :
- temp
- long_name :
- TEMPERATURE (C)
- name :
- T
- standard_name :
- sea_water_temperature
- units :
- C
Array Chunk Bytes 61.20 MiB 33.40 MiB Shape (131496, 61) (71762, 61) Dask graph 2 chunks in 4 graph layers Data type float64 numpy.ndarray - dens(time, depth)float64dask.array<chunksize=(71762, 61), meta=np.ndarray>
- long_name :
- $ρ$
- standard_name :
- sea_water_potential_density
- units :
- kg/m3
Array Chunk Bytes 61.20 MiB 33.40 MiB Shape (131496, 61) (71762, 61) Dask graph 2 chunks in 4 graph layers Data type float64 numpy.ndarray - densT(time, depth)float64dask.array<chunksize=(71762, 61), meta=np.ndarray>
- description :
- density using T, S
- long_name :
- $ρ_T$
- standard_name :
- sea_water_potential_density
- units :
- kg/m3
Array Chunk Bytes 61.20 MiB 33.40 MiB Shape (131496, 61) (71762, 61) Dask graph 2 chunks in 4 graph layers Data type float64 numpy.ndarray - lwnet(time)float32dask.array<chunksize=(131496,), meta=np.ndarray>
- FORTRAN_format :
- epic_code :
- 1136
- generic_name :
- qln
- long_name :
- NET LONGWAVE RADIATION
- name :
- LWN
- units :
- W m-2
Array Chunk Bytes 513.66 kiB 513.66 kiB Shape (131496,) (131496,) Dask graph 1 chunks in 4 graph layers Data type float32 numpy.ndarray - qlat(time)float32dask.array<chunksize=(131496,), meta=np.ndarray>
- FORTRAN_format :
- epic_code :
- 137
- generic_name :
- qlat
- long_name :
- LATENT HEAT FLUX
- name :
- QL
- units :
- W m-2
Array Chunk Bytes 513.66 kiB 513.66 kiB Shape (131496,) (131496,) Dask graph 1 chunks in 4 graph layers Data type float32 numpy.ndarray - qnet(time)float32dask.array<chunksize=(131496,), meta=np.ndarray>
- FORTRAN_format :
- epic_code :
- 210
- generic_name :
- qtot
- long_name :
- TOTAL HEAT FLUX
- name :
- QT
- units :
- W/M**2
- standard_name :
- surface_downward_heat_flux_in_sea_water
Array Chunk Bytes 513.66 kiB 513.66 kiB Shape (131496,) (131496,) Dask graph 1 chunks in 4 graph layers Data type float32 numpy.ndarray - qsen(time)float32dask.array<chunksize=(131496,), meta=np.ndarray>
- FORTRAN_format :
- epic_code :
- 138
- generic_name :
- qsen
- long_name :
- SENSIBLE HEAT FLUX
- name :
- QS
- units :
- W m-2
Array Chunk Bytes 513.66 kiB 513.66 kiB Shape (131496,) (131496,) Dask graph 1 chunks in 4 graph layers Data type float32 numpy.ndarray - swnet(time)float32dask.array<chunksize=(131496,), meta=np.ndarray>
- FORTRAN_format :
- epic_code :
- 1495
- generic_name :
- sw
- long_name :
- NET SHORTWAVE RADIATION
- name :
- SWN
- units :
- W/M**2
- standard_name :
- net_downward_shortwave_flux_at_sea_water_surface
Array Chunk Bytes 513.66 kiB 513.66 kiB Shape (131496,) (131496,) Dask graph 1 chunks in 4 graph layers Data type float32 numpy.ndarray - tau(time)float64dask.array<chunksize=(131496,), meta=np.ndarray>
Array Chunk Bytes 1.00 MiB 1.00 MiB Shape (131496,) (131496,) Dask graph 1 chunks in 8 graph layers Data type float64 numpy.ndarray - taux(time)float64dask.array<chunksize=(131496,), meta=np.ndarray>
- standard_name :
- surface_downward_x_stress
Array Chunk Bytes 1.00 MiB 1.00 MiB Shape (131496,) (131496,) Dask graph 1 chunks in 4 graph layers Data type float64 numpy.ndarray - tauy(time)float64dask.array<chunksize=(131496,), meta=np.ndarray>
- standard_name :
- surface_downward_y_stress
Array Chunk Bytes 1.00 MiB 1.00 MiB Shape (131496,) (131496,) Dask graph 1 chunks in 4 graph layers Data type float64 numpy.ndarray - theta(time, depth)float64dask.array<chunksize=(71762, 61), meta=np.ndarray>
- description :
- potential temperature using T, S=35
- long_name :
- $θ$
- standard_name :
- sea_water_potential_temperature
- units :
- degC
Array Chunk Bytes 61.20 MiB 33.40 MiB Shape (131496, 61) (71762, 61) Dask graph 2 chunks in 4 graph layers Data type float64 numpy.ndarray - u(time, depth)float32dask.array<chunksize=(71762, 61), meta=np.ndarray>
- FORTRAN_format :
- epic_code :
- 1205
- generic_name :
- u
- long_name :
- u
- name :
- u
- standard_name :
- sea_water_x_velocity
- units :
- m/s
Array Chunk Bytes 30.60 MiB 16.70 MiB Shape (131496, 61) (71762, 61) Dask graph 2 chunks in 4 graph layers Data type float32 numpy.ndarray - v(time, depth)float32dask.array<chunksize=(71762, 61), meta=np.ndarray>
- FORTRAN_format :
- epic_code :
- 1206
- generic_name :
- v
- long_name :
- v
- name :
- v
- standard_name :
- sea_water_y_velocity
- units :
- m/s
Array Chunk Bytes 30.60 MiB 16.70 MiB Shape (131496, 61) (71762, 61) Dask graph 2 chunks in 4 graph layers Data type float32 numpy.ndarray - wind_dir(time)float32dask.array<chunksize=(131496,), meta=np.ndarray>
- FORTRAN_format :
- epic_code :
- 410
- generic_name :
- long_name :
- WIND DIRECTION
- name :
- WD
- standard_name :
- wind_from_direction
- units :
- degrees
Array Chunk Bytes 513.66 kiB 513.66 kiB Shape (131496,) (131496,) Dask graph 1 chunks in 4 graph layers Data type float32 numpy.ndarray - pressure(depth)float64301.9 296.8 291.8 ... 5.028 -0.0
- standard_name :
- sea_water_pressure
- units :
- dbar
array([301.87732362, 296.84242473, 291.80764803, 286.77299352, 281.73846121, 276.70405112, 271.66976325, 266.63559761, 261.60155422, 256.56763308, 251.5338342 , 246.5001576 , 241.46660329, 236.43317126, 231.39986155, 226.36667414, 221.33360906, 216.30066632, 211.26784592, 206.23514788, 201.2025722 , 196.17011889, 191.13778797, 186.10557945, 181.07349333, 176.04152963, 171.00968835, 165.97796951, 160.94637311, 155.91489917, 150.8835477 , 145.8523187 , 140.82121218, 135.79022817, 130.75936665, 125.72862766, 120.69801119, 115.66751726, 110.63714587, 105.60689704, 100.57677078, 95.54676709, 90.51688599, 85.48712749, 80.4574916 , 75.42797832, 70.39858766, 65.36931965, 60.34017428, 55.31115157, 50.28225153, 45.25347416, 40.22481948, 35.1962875 , 30.16787822, 25.13959167, 20.11142784, 15.08338675, 10.0554684 , 5.02767282, -0. ]) - SA(time, depth)float64dask.array<chunksize=(71762, 61), meta=np.ndarray>
- standard_name :
- sea_water_absolute_salinity
- units :
- g/kg
Array Chunk Bytes 61.20 MiB 33.40 MiB Shape (131496, 61) (71762, 61) Dask graph 2 chunks in 6 graph layers Data type float64 numpy.ndarray - CT(time, depth)float64dask.array<chunksize=(71762, 61), meta=np.ndarray>
- standard_name :
- sea_water_conservative_temperature
- units :
- degC
- reference_scale :
- ITS-90
Array Chunk Bytes 61.20 MiB 33.40 MiB Shape (131496, 61) (71762, 61) Dask graph 2 chunks in 10 graph layers Data type float64 numpy.ndarray - α(time, depth)float64dask.array<chunksize=(71762, 61), meta=np.ndarray>
- units :
- 1/K
- standard_name :
- sea_water_thermal_expansion_coefficient
Array Chunk Bytes 61.20 MiB 33.40 MiB Shape (131496, 61) (71762, 61) Dask graph 2 chunks in 11 graph layers Data type float64 numpy.ndarray - β(time, depth)float64dask.array<chunksize=(71762, 61), meta=np.ndarray>
- units :
- kg/g
- standard_name :
- sea_water_haline_contraction_coefficient
Array Chunk Bytes 61.20 MiB 33.40 MiB Shape (131496, 61) (71762, 61) Dask graph 2 chunks in 11 graph layers Data type float64 numpy.ndarray - Tz(time, depth)float64dask.array<chunksize=(71762, 61), meta=np.ndarray>
- long_name :
- $T_z$
- units :
- ℃/m
Array Chunk Bytes 61.20 MiB 33.40 MiB Shape (131496, 61) (71762, 61) Dask graph 2 chunks in 9 graph layers Data type float64 numpy.ndarray - Sz(time, depth)float64dask.array<chunksize=(71762, 61), meta=np.ndarray>
- long_name :
- $S_z$
- units :
- g/kg/m
Array Chunk Bytes 61.20 MiB 33.40 MiB Shape (131496, 61) (71762, 61) Dask graph 2 chunks in 9 graph layers Data type float64 numpy.ndarray - chi(time, depthchi)float64nan nan nan nan ... nan nan nan nan
array([[nan, nan, nan, nan, nan, nan], [nan, nan, nan, nan, nan, nan], [nan, nan, nan, nan, nan, nan], ..., [nan, nan, nan, nan, nan, nan], [nan, nan, nan, nan, nan, nan], [nan, nan, nan, nan, nan, nan]]) - KT(time, depthchi)float64nan nan nan nan ... nan nan nan nan
- standard_name :
- ocean_vertical_heat_diffusivity
array([[nan, nan, nan, nan, nan, nan], [nan, nan, nan, nan, nan, nan], [nan, nan, nan, nan, nan, nan], ..., [nan, nan, nan, nan, nan, nan], [nan, nan, nan, nan, nan, nan], [nan, nan, nan, nan, nan, nan]]) - eps(time, depthchi)float64nan nan nan nan ... nan nan nan nan
array([[nan, nan, nan, nan, nan, nan], [nan, nan, nan, nan, nan, nan], [nan, nan, nan, nan, nan, nan], ..., [nan, nan, nan, nan, nan, nan], [nan, nan, nan, nan, nan, nan], [nan, nan, nan, nan, nan, nan]]) - Jq(time, depthchi)float64nan nan nan nan ... nan nan nan nan
array([[nan, nan, nan, nan, nan, nan], [nan, nan, nan, nan, nan, nan], [nan, nan, nan, nan, nan, nan], ..., [nan, nan, nan, nan, nan, nan], [nan, nan, nan, nan, nan, nan], [nan, nan, nan, nan, nan, nan]]) - shred2(time, depth)float64dask.array<chunksize=(71762, 61), meta=np.ndarray>
- long_name :
- $Sh_{red}^2$
- units :
- $s^{-2}$
Array Chunk Bytes 61.20 MiB 33.40 MiB Shape (131496, 61) (71762, 61) Dask graph 2 chunks in 9 graph layers Data type float64 numpy.ndarray - Rig(time, depth)float64dask.array<chunksize=(71762, 61), meta=np.ndarray>
- long_name :
- $Ri^g$
Array Chunk Bytes 61.20 MiB 33.40 MiB Shape (131496, 61) (71762, 61) Dask graph 2 chunks in 8 graph layers Data type float64 numpy.ndarray - sst(time)float64dask.array<chunksize=(71762,), meta=np.ndarray>
- description :
- potential temperature using T, S=35
- long_name :
- $SST$
- standard_name :
- sea_surface_temperature
- units :
- degC
Array Chunk Bytes 1.00 MiB 560.64 kiB Shape (131496,) (71762,) Dask graph 2 chunks in 5 graph layers Data type float64 numpy.ndarray - Tflx_dia_diff(time, depthchi)float64nan nan nan nan ... nan nan nan nan
- standard_name :
- ocean_vertical_diffusive_heat_flux
array([[nan, nan, nan, nan, nan, nan], [nan, nan, nan, nan, nan, nan], [nan, nan, nan, nan, nan, nan], ..., [nan, nan, nan, nan, nan, nan], [nan, nan, nan, nan, nan, nan], [nan, nan, nan, nan, nan, nan]]) - ν(time, depthchi)float64dask.array<chunksize=(71762, 6), meta=np.ndarray>
- standard_name :
- ocean_vertical_momentum_diffusivity
Array Chunk Bytes 6.02 MiB 3.29 MiB Shape (131496, 6) (71762, 6) Dask graph 2 chunks in 13 graph layers Data type float64 numpy.ndarray - Rif(time, depthchi)float64dask.array<chunksize=(71762, 6), meta=np.ndarray>
- standard_name :
- flux_richardson_number
Array Chunk Bytes 6.02 MiB 3.29 MiB Shape (131496, 6) (71762, 6) Dask graph 2 chunks in 53 graph layers Data type float64 numpy.ndarray - Jb(time, depthchi)float64dask.array<chunksize=(71762, 6), meta=np.ndarray>
- standard_name :
- ocean_vertical_diffusive_buoyancy_flux
Array Chunk Bytes 6.02 MiB 3.29 MiB Shape (131496, 6) (71762, 6) Dask graph 2 chunks in 50 graph layers Data type float64 numpy.ndarray
- CREATION_DATE :
- 23:26 24-FEB-2021
- Data_Source :
- Global Tropical Moored Buoy Array Project Office/NOAA/PMEL
- File_info :
- Contact: Dai.C.McClurg@noaa.gov
- Request_for_acknowledgement :
- If you use these data in publications or presentations, please acknowledge the GTMBA Project Office of NOAA/PMEL. Also, we would appreciate receiving a preprint and/or reprint of publications utilizing the data for inclusion in our bibliography. Relevant publications should be sent to: GTMBA Project Office, NOAA/Pacific Marine Environmental Laboratory, 7600 Sand Point Way NE, Seattle, WA 98115
- _FillValue :
- 1.0000000409184788e+35
- array :
- TAO/TRITON
- missing_value :
- 1.0000000409184788e+35
- platform_code :
- 0n165e
- site_code :
- 0n165e
- wmo_platform_code :
- 52321
<xarray.DatasetView> Dimensions: (time: 131496, depth: 61, depthchi: 6) Coordinates: (12/19) deepest (time) float64 dask.array<chunksize=(131496,), meta=np.ndarray> * depth (depth) float64 -300.0 -295.0 -290.0 ... -10.0 -5.0 0.0 eucmax (time) float64 dask.array<chunksize=(71762,), meta=np.ndarray> latitude float32 0.0 longitude float32 -140.0 mld (time) float64 dask.array<chunksize=(131496,), meta=np.ndarray> ... ... oni (time) float32 0.92 0.92 0.92 0.92 ... -0.87 -0.87 -0.87 en_mask (time) bool True True True True ... False False False ln_mask (time) bool False False False False ... True True True warm_mask (time) bool False False False False ... True True True cool_mask (time) bool True True True True ... False False False enso_transition (time) <U12 'El-Nino cool' ... 'La-Nina warm' Data variables: (12/39) N2 (time, depth) float64 dask.array<chunksize=(71762, 61), meta=np.ndarray> N2T (time, depth) float64 dask.array<chunksize=(71762, 61), meta=np.ndarray> Ri (time, depth) float64 dask.array<chunksize=(71762, 61), meta=np.ndarray> Rig_T (time, depth) float64 dask.array<chunksize=(71762, 61), meta=np.ndarray> S (time, depth) float64 dask.array<chunksize=(71762, 61), meta=np.ndarray> S2 (time, depth) float32 dask.array<chunksize=(71762, 61), meta=np.ndarray> ... ... Rig (time, depth) float64 dask.array<chunksize=(71762, 61), meta=np.ndarray> sst (time) float64 dask.array<chunksize=(71762,), meta=np.ndarray> Tflx_dia_diff (time, depthchi) float64 nan nan nan nan ... nan nan nan ν (time, depthchi) float64 dask.array<chunksize=(71762, 6), meta=np.ndarray> Rif (time, depthchi) float64 dask.array<chunksize=(71762, 6), meta=np.ndarray> Jb (time, depthchi) float64 dask.array<chunksize=(71762, 6), meta=np.ndarray> Attributes: CREATION_DATE: 23:26 24-FEB-2021 Data_Source: Global Tropical Moored Buoy Array Project O... File_info: Contact: Dai.C.McClurg@noaa.gov Request_for_acknowledgement: If you use these data in publications or pr... _FillValue: 1.0000000409184788e+35 array: TAO/TRITON missing_value: 1.0000000409184788e+35 platform_code: 0n165e site_code: 0n165e wmo_platform_code: 52321TAO- time: 131400
- zl: 37
- zi: 37
- nv: 2
- nv(nv)float641.0 2.0
- long_name :
- vertex number
array([1., 2.])
- time(time)datetime64[ns]2003-01-01T00:30:00 ... 2017-12-...
array(['2003-01-01T00:30:00.000000000', '2003-01-01T01:30:00.000000000', '2003-01-01T02:30:00.000000000', ..., '2017-12-31T21:30:00.000000000', '2017-12-31T22:30:00.000000000', '2017-12-31T23:30:00.000000000'], dtype='datetime64[ns]') - xh()float64-140.0
- axis :
- X
- domain_decomposition :
- [220, 222, 220, 221]
- long_name :
- h point nominal longitude
- units :
- degrees_east
array(-140.)
- yh()float640.0625
- axis :
- Y
- domain_decomposition :
- [210, 258, 210, 221]
- long_name :
- h point nominal latitude
- units :
- degrees_north
array(0.06249997)
- yq()float64-0.0625
- axis :
- Y
- domain_decomposition :
- [209, 257, 209, 221]
- long_name :
- q point nominal latitude
- units :
- degrees_north
array(-0.06249997)
- zi(zi)float64-523.8 -481.0 -442.5 ... -2.5 -0.0
- axis :
- Z
- long_name :
- Interface pseudo-depth, -z*
- positive :
- up
- units :
- meter
array([-523.8 , -481.01, -442.51, -407.64, -375.88, -346.78, -319.99, -295.22, -272.22, -250.8 , -230.78, -212.02, -194.41, -177.85, -162.26, -147.57, -133.72, -120.66, -108.37, -96.83, -86.02, -75.94, -66.57, -57.91, -49.94, -42.66, -36.05, -30.1 , -24.81, -20.16, -16.15, -12.77, -10. , -7.5 , -5. , -2.5 , -0. ]) - zl(zl)float64-547.8 -502.4 ... -3.75 -1.25
- axis :
- Z
- long_name :
- Layer pseudo-depth, -z*
- positive :
- up
- units :
- meter
array([-547.75 , -502.405, -461.76 , -425.075, -391.76 , -361.33 , -333.385, -307.605, -283.72 , -261.51 , -240.79 , -221.4 , -203.215, -186.13 , -170.055, -154.915, -140.645, -127.19 , -114.515, -102.6 , -91.425, -80.98 , -71.255, -62.24 , -53.925, -46.3 , -39.355, -33.075, -27.455, -22.485, -18.155, -14.46 , -11.385, -8.75 , -6.25 , -3.75 , -1.25 ]) - eucmax(time)float64dask.array<chunksize=(8760,), meta=np.ndarray>
- units :
- m
- long_name :
- EUC maximum
- positive :
- up
Array Chunk Bytes 1.00 MiB 68.44 kiB Shape (131400,) (8760,) Dask graph 15 chunks in 22 graph layers Data type float64 numpy.ndarray - mldT(time)float64dask.array<chunksize=(8760,), meta=np.ndarray>
- long_name :
- MLD$_θ$
- units :
- m
- description :
- Interpolate θi to 1m grid. Search for max depth where |dθ| > 0.15
Array Chunk Bytes 1.00 MiB 68.44 kiB Shape (131400,) (8760,) Dask graph 15 chunks in 24 graph layers Data type float64 numpy.ndarray - dcl_mask(zi, time)booldask.array<chunksize=(37, 8760), meta=np.ndarray>
- description :
- True when 5m below mldT and above eucmax.
Array Chunk Bytes 4.64 MiB 316.52 kiB Shape (37, 131400) (37, 8760) Dask graph 15 chunks in 57 graph layers Data type bool numpy.ndarray - oni(time)float320.92 0.92 0.92 ... -0.87 -0.87
array([ 0.92, 0.92, 0.92, ..., -0.87, -0.87, -0.87], dtype=float32)
- en_mask(time)boolTrue True True ... False False
array([ True, True, True, ..., False, False, False])
- ln_mask(time)boolFalse False False ... True True
array([False, False, False, ..., True, True, True])
- warm_mask(time)boolFalse False False ... True True
array([False, False, False, ..., True, True, True])
- cool_mask(time)boolTrue True True ... False False
array([ True, True, True, ..., False, False, False])
- enso_transition(time)<U12'El-Nino cool' ... 'La-Nina warm'
- description :
- Warner & Moum (2019) ENSO transition phase; El-Nino = ONI > 0.5 for at least 6 months; La-Nina = ONI < -0.5 for at least 6 months
array(['El-Nino cool', 'El-Nino cool', 'El-Nino cool', ..., 'La-Nina warm', 'La-Nina warm', 'La-Nina warm'], dtype='<U12')
- KPP_BulkRi(time, zl)float32dask.array<chunksize=(8760, 37), meta=np.ndarray>
- cell_measures :
- volume: volcello area: areacello
- cell_methods :
- area:mean zl:mean yh:mean xh:mean time: mean
- long_name :
- Bulk Richardson number used to find the OBL depth used by [CVMix] KPP
- time_avg_info :
- average_T1,average_T2,average_DT
- units :
- nondim
Array Chunk Bytes 18.55 MiB 1.24 MiB Shape (131400, 37) (8760, 37) Dask graph 15 chunks in 6 graph layers Data type float32 numpy.ndarray - KPP_N2(time, zi)float32dask.array<chunksize=(8760, 37), meta=np.ndarray>
- cell_measures :
- area: areacello
- cell_methods :
- area:mean zi:point yh:mean xh:mean time: mean
- long_name :
- Square of Brunt-Vaisala frequency used by [CVMix] KPP
- time_avg_info :
- average_T1,average_T2,average_DT
- units :
- 1/s2
Array Chunk Bytes 18.55 MiB 1.24 MiB Shape (131400, 37) (8760, 37) Dask graph 15 chunks in 6 graph layers Data type float32 numpy.ndarray - KPP_NLT_temp_budget(time, zl)float32dask.array<chunksize=(8760, 37), meta=np.ndarray>
- cell_measures :
- volume: volcello area: areacello
- cell_methods :
- area:mean zl:sum yh:mean xh:mean time: mean
- long_name :
- Heat content change due to non-local transport, as calculated by [CVMix] KPP
- time_avg_info :
- average_T1,average_T2,average_DT
- units :
- W m-2
Array Chunk Bytes 18.55 MiB 1.24 MiB Shape (131400, 37) (8760, 37) Dask graph 15 chunks in 6 graph layers Data type float32 numpy.ndarray - KPP_NLtransport_heat(time, zi)float32dask.array<chunksize=(8760, 37), meta=np.ndarray>
- cell_measures :
- area: areacello
- cell_methods :
- area:mean zi:point yh:mean xh:mean time: mean
- long_name :
- Non-local transport (Cs*G(sigma)) for heat, as calculated by [CVMix] KPP
- time_avg_info :
- average_T1,average_T2,average_DT
- units :
- nondim
Array Chunk Bytes 18.55 MiB 1.24 MiB Shape (131400, 37) (8760, 37) Dask graph 15 chunks in 6 graph layers Data type float32 numpy.ndarray - KPP_OBLdepth(time)float32dask.array<chunksize=(131400,), meta=np.ndarray>
- cell_measures :
- area: areacello
- cell_methods :
- area:mean yh:mean xh:mean time: mean
- long_name :
- Thickness of the surface Ocean Boundary Layer calculated by [CVMix] KPP
- time_avg_info :
- average_T1,average_T2,average_DT
- units :
- meter
Array Chunk Bytes 513.28 kiB 513.28 kiB Shape (131400,) (131400,) Dask graph 1 chunks in 4 graph layers Data type float32 numpy.ndarray - KPP_buoyFlux(time, zi)float32dask.array<chunksize=(8760, 37), meta=np.ndarray>
- cell_measures :
- area: areacello
- cell_methods :
- area:mean zi:point yh:mean xh:mean time: mean
- long_name :
- Surface (and penetrating) buoyancy flux, as used by [CVMix] KPP
- time_avg_info :
- average_T1,average_T2,average_DT
- units :
- m2/s3
Array Chunk Bytes 18.55 MiB 1.24 MiB Shape (131400, 37) (8760, 37) Dask graph 15 chunks in 6 graph layers Data type float32 numpy.ndarray - KPP_kheat(time, zi)float32dask.array<chunksize=(8760, 37), meta=np.ndarray>
- cell_measures :
- area: areacello
- cell_methods :
- area:mean zi:point yh:mean xh:mean time: mean
- long_name :
- Heat diffusivity due to KPP, as calculated by [CVMix] KPP
- time_avg_info :
- average_T1,average_T2,average_DT
- units :
- m2/s
Array Chunk Bytes 18.55 MiB 1.24 MiB Shape (131400, 37) (8760, 37) Dask graph 15 chunks in 6 graph layers Data type float32 numpy.ndarray - Kd_heat(time, zi)float32dask.array<chunksize=(8760, 37), meta=np.ndarray>
- cell_measures :
- area: areacello
- cell_methods :
- area:mean zi:point yh:mean xh:mean time: mean
- long_name :
- Total diapycnal diffusivity for heat at interfaces
- standard_name :
- ocean_vertical_heat_diffusivity
- time_avg_info :
- average_T1,average_T2,average_DT
- units :
- m2 s-1
Array Chunk Bytes 18.55 MiB 1.24 MiB Shape (131400, 37) (8760, 37) Dask graph 15 chunks in 6 graph layers Data type float32 numpy.ndarray - Kv_u(time, zl)float32dask.array<chunksize=(8760, 37), meta=np.ndarray>
- cell_methods :
- zl:mean yh:mean xq:point time: mean
- interp_method :
- none
- long_name :
- Total vertical viscosity at u-points
- standard_name :
- ocean_vertical_x_viscosity
- time_avg_info :
- average_T1,average_T2,average_DT
- units :
- m2 s-1
Array Chunk Bytes 18.55 MiB 1.24 MiB Shape (131400, 37) (8760, 37) Dask graph 15 chunks in 6 graph layers Data type float32 numpy.ndarray - Kv_v(time, zl)float32dask.array<chunksize=(8760, 37), meta=np.ndarray>
- cell_methods :
- zl:mean yq:point xh:mean time: mean
- interp_method :
- none
- long_name :
- Total vertical viscosity at v-points
- standard_name :
- ocean_vertical_y_viscosity
- time_avg_info :
- average_T1,average_T2,average_DT
- units :
- m2 s-1
Array Chunk Bytes 18.55 MiB 1.24 MiB Shape (131400, 37) (8760, 37) Dask graph 15 chunks in 6 graph layers Data type float32 numpy.ndarray - N2(time, zi)float32dask.array<chunksize=(8760, 37), meta=np.ndarray>
- cell_measures :
- area: areacello
- cell_methods :
- area:mean zi:point yh:mean xh:mean time: mean
- long_name :
- Buoyancy frequency squared
- time_avg_info :
- average_T1,average_T2,average_DT
- units :
- s-2
Array Chunk Bytes 18.55 MiB 1.24 MiB Shape (131400, 37) (8760, 37) Dask graph 15 chunks in 6 graph layers Data type float32 numpy.ndarray - N2_shear(time, zi)float32dask.array<chunksize=(8760, 37), meta=np.ndarray>
- cell_measures :
- area: areacello
- cell_methods :
- area:mean zi:point yh:mean xh:mean time: mean
- long_name :
- Square of Brunt-Vaisala frequency used by MOM_CVMix_shear module
- time_avg_info :
- average_T1,average_T2,average_DT
- units :
- 1/s2
Array Chunk Bytes 18.55 MiB 1.24 MiB Shape (131400, 37) (8760, 37) Dask graph 15 chunks in 6 graph layers Data type float32 numpy.ndarray - S2_shear(time, zi)float32dask.array<chunksize=(8760, 37), meta=np.ndarray>
- cell_measures :
- area: areacello
- cell_methods :
- area:mean zi:point yh:mean xh:mean time: mean
- long_name :
- Square of vertical shear used by MOM_CVMix_shear module
- time_avg_info :
- average_T1,average_T2,average_DT
- units :
- 1/s2
Array Chunk Bytes 18.55 MiB 1.24 MiB Shape (131400, 37) (8760, 37) Dask graph 15 chunks in 6 graph layers Data type float32 numpy.ndarray - SSH(time)float32dask.array<chunksize=(131400,), meta=np.ndarray>
- cell_measures :
- area: areacello
- cell_methods :
- area:mean yh:mean xh:mean time: mean
- long_name :
- Sea Surface Height
- time_avg_info :
- average_T1,average_T2,average_DT
- units :
- m
Array Chunk Bytes 513.28 kiB 513.28 kiB Shape (131400,) (131400,) Dask graph 1 chunks in 4 graph layers Data type float32 numpy.ndarray - SW(time)float32dask.array<chunksize=(131400,), meta=np.ndarray>
- cell_measures :
- area: areacello
- cell_methods :
- area:mean yh:mean xh:mean time: mean
- long_name :
- Shortwave radiation flux into ocean
- standard_name :
- net_downward_shortwave_flux_at_sea_water_surface
- time_avg_info :
- average_T1,average_T2,average_DT
- units :
- W m-2
Array Chunk Bytes 513.28 kiB 513.28 kiB Shape (131400,) (131400,) Dask graph 1 chunks in 4 graph layers Data type float32 numpy.ndarray - SW_pen(time)float32dask.array<chunksize=(131400,), meta=np.ndarray>
- cell_measures :
- area: areacello
- cell_methods :
- area:mean yh:mean xh:mean time: mean
- long_name :
- Penetrating shortwave radiation flux into ocean
- time_avg_info :
- average_T1,average_T2,average_DT
- units :
- W m-2
Array Chunk Bytes 513.28 kiB 513.28 kiB Shape (131400,) (131400,) Dask graph 1 chunks in 4 graph layers Data type float32 numpy.ndarray - T_advection_xy(time, zl)float32dask.array<chunksize=(8760, 37), meta=np.ndarray>
- cell_measures :
- volume: volcello area: areacello
- cell_methods :
- area:mean zl:sum yh:mean xh:mean time: mean
- long_name :
- Horizontal convergence of residual mean advective fluxes of heat
- time_avg_info :
- average_T1,average_T2,average_DT
- units :
- W m-2
Array Chunk Bytes 18.55 MiB 1.24 MiB Shape (131400, 37) (8760, 37) Dask graph 15 chunks in 6 graph layers Data type float32 numpy.ndarray - T_lbdxy_cont_tendency(time, zl)float32dask.array<chunksize=(8760, 37), meta=np.ndarray>
- cell_measures :
- volume: volcello area: areacello
- cell_methods :
- area:sum zl:sum yh:sum xh:sum time: mean
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- units :
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Array Chunk Bytes 18.55 MiB 1.24 MiB Shape (131400, 37) (8760, 37) Dask graph 15 chunks in 6 graph layers Data type float32 numpy.ndarray - Tflx_dia_diff(time, zi)float32dask.array<chunksize=(8760, 37), meta=np.ndarray>
- cell_measures :
- area: areacello
- cell_methods :
- area:mean zi:point yh:mean xh:mean time: mean
- long_name :
- Diffusive diapycnal temperature flux across interfaces
- standard_name :
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- time_avg_info :
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- units :
- degC m s-1
Array Chunk Bytes 18.55 MiB 1.24 MiB Shape (131400, 37) (8760, 37) Dask graph 15 chunks in 6 graph layers Data type float32 numpy.ndarray - Th_tendency_vert_remap(time, zl)float32dask.array<chunksize=(8760, 37), meta=np.ndarray>
- cell_measures :
- volume: volcello area: areacello
- cell_methods :
- area:mean zl:sum yh:mean xh:mean time: mean
- long_name :
- Vertical remapping tracer content tendency for Heat
- time_avg_info :
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- units :
- W m-2
Array Chunk Bytes 18.55 MiB 1.24 MiB Shape (131400, 37) (8760, 37) Dask graph 15 chunks in 6 graph layers Data type float32 numpy.ndarray - boundary_forcing_heat_tendency(time, zl)float32dask.array<chunksize=(8760, 37), meta=np.ndarray>
- cell_measures :
- volume: volcello area: areacello
- cell_methods :
- area:mean zl:sum yh:mean xh:mean time: mean
- long_name :
- Boundary forcing heat tendency
- time_avg_info :
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- units :
- W m-2
Array Chunk Bytes 18.55 MiB 1.24 MiB Shape (131400, 37) (8760, 37) Dask graph 15 chunks in 6 graph layers Data type float32 numpy.ndarray - dens(time, zl)float32dask.array<chunksize=(8760, 37), meta=np.ndarray>
- cell_measures :
- volume: volcello area: areacello
- cell_methods :
- area:mean zl:mean yh:mean xh:mean time: mean
- long_name :
- Sea Water Salinity
- standard_name :
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- time_avg_info :
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- units :
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Array Chunk Bytes 18.55 MiB 1.24 MiB Shape (131400, 37) (8760, 37) Dask graph 15 chunks in 6 graph layers Data type float32 numpy.ndarray - densT(time, zl)float32dask.array<chunksize=(8760, 37), meta=np.ndarray>
- cell_measures :
- volume: volcello area: areacello
- cell_methods :
- area:mean zl:mean yh:mean xh:mean time: mean
- long_name :
- Sea Water Potential Temperature
- standard_name :
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- units :
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Array Chunk Bytes 18.55 MiB 1.24 MiB Shape (131400, 37) (8760, 37) Dask graph 15 chunks in 6 graph layers Data type float32 numpy.ndarray - frazil_heat_tendency(time, zl)float32dask.array<chunksize=(8760, 37), meta=np.ndarray>
- cell_measures :
- volume: volcello area: areacello
- cell_methods :
- area:mean zl:sum yh:mean xh:mean time: mean
- long_name :
- Heat tendency due to frazil formation
- time_avg_info :
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- units :
- W m-2
Array Chunk Bytes 18.55 MiB 1.24 MiB Shape (131400, 37) (8760, 37) Dask graph 15 chunks in 6 graph layers Data type float32 numpy.ndarray - h(time, zl)float32dask.array<chunksize=(8760, 37), meta=np.ndarray>
- cell_measures :
- volume: volcello area: areacello
- cell_methods :
- area:mean zl:sum yh:mean xh:mean time: mean
- long_name :
- Layer Thickness
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- units :
- m
Array Chunk Bytes 18.55 MiB 1.24 MiB Shape (131400, 37) (8760, 37) Dask graph 15 chunks in 6 graph layers Data type float32 numpy.ndarray - mlotst(time)float32dask.array<chunksize=(131400,), meta=np.ndarray>
- cell_measures :
- area: areacello
- cell_methods :
- area:mean yh:mean xh:mean time: mean
- long_name :
- Ocean Mixed Layer Thickness Defined by Sigma T
- standard_name :
- ocean_mixed_layer_thickness_defined_by_sigma_t
- time_avg_info :
- average_T1,average_T2,average_DT
- units :
- m
Array Chunk Bytes 513.28 kiB 513.28 kiB Shape (131400,) (131400,) Dask graph 1 chunks in 4 graph layers Data type float32 numpy.ndarray - net_heat_surface(time)float32dask.array<chunksize=(131400,), meta=np.ndarray>
- cell_measures :
- area: areacello
- cell_methods :
- area:mean yh:mean xh:mean time: mean
- long_name :
- Surface ocean heat flux from SW+LW+lat+sens+mass transfer+frazil+restore+seaice_melt_heat or flux adjustments
- standard_name :
- surface_downward_heat_flux_in_sea_water
- time_avg_info :
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- units :
- W m-2
Array Chunk Bytes 513.28 kiB 513.28 kiB Shape (131400,) (131400,) Dask graph 1 chunks in 4 graph layers Data type float32 numpy.ndarray - opottempdiff(time, zl)float32dask.array<chunksize=(8760, 37), meta=np.ndarray>
- cell_measures :
- volume: volcello area: areacello
- cell_methods :
- area:mean zl:sum yh:mean xh:mean time: mean
- long_name :
- Tendency of sea water potential temperature expressed as heat content due to parameterized dianeutral mixing
- standard_name :
- tendency_of_sea_water_potential_temperature_expressed_as_heat_content_due_to_parameterized_dianeutral_mixing
- time_avg_info :
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- units :
- W m-2
Array Chunk Bytes 18.55 MiB 1.24 MiB Shape (131400, 37) (8760, 37) Dask graph 15 chunks in 6 graph layers Data type float32 numpy.ndarray - opottemppmdiff(time, zl)float32dask.array<chunksize=(8760, 37), meta=np.ndarray>
- cell_measures :
- volume: volcello area: areacello
- cell_methods :
- area:sum zl:sum yh:sum xh:sum time: mean
- long_name :
- Tendency of sea water potential temperature expressed as heat content due to parameterized mesoscale neutral diffusion
- standard_name :
- tendency_of_sea_water_potential_temperature_expressed_as_heat_content_due_to_parameterized_mesoscale_neutral_diffusion
- time_avg_info :
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- units :
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Array Chunk Bytes 18.55 MiB 1.24 MiB Shape (131400, 37) (8760, 37) Dask graph 15 chunks in 6 graph layers Data type float32 numpy.ndarray - opottemptend(time, zl)float32dask.array<chunksize=(8760, 37), meta=np.ndarray>
- cell_measures :
- volume: volcello area: areacello
- cell_methods :
- area:mean zl:sum yh:mean xh:mean time: mean
- long_name :
- Tendency of Sea Water Potential Temperature Expressed as Heat Content
- standard_name :
- tendency_of_sea_water_potential_temperature_expressed_as_heat_content
- time_avg_info :
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- units :
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Array Chunk Bytes 18.55 MiB 1.24 MiB Shape (131400, 37) (8760, 37) Dask graph 15 chunks in 6 graph layers Data type float32 numpy.ndarray - ri_grad_shear(time, zi)float32dask.array<chunksize=(8760, 37), meta=np.ndarray>
- cell_measures :
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- cell_methods :
- area:mean zi:point yh:mean xh:mean time: mean
- long_name :
- Gradient Richarson number used by MOM_CVMix_shear module
- time_avg_info :
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- units :
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Array Chunk Bytes 18.55 MiB 1.24 MiB Shape (131400, 37) (8760, 37) Dask graph 15 chunks in 6 graph layers Data type float32 numpy.ndarray - ri_grad_shear_orig(time, zi)float32dask.array<chunksize=(8760, 37), meta=np.ndarray>
- cell_measures :
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- cell_methods :
- area:mean zi:point yh:mean xh:mean time: mean
- long_name :
- Original gradient Richarson number, before smoothing was applied. This is part of the MOM_CVMix_shear module and only available
- time_avg_info :
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- units :
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Array Chunk Bytes 18.55 MiB 1.24 MiB Shape (131400, 37) (8760, 37) Dask graph 15 chunks in 6 graph layers Data type float32 numpy.ndarray - so(time, zl)float32dask.array<chunksize=(8760, 37), meta=np.ndarray>
- cell_measures :
- volume: volcello area: areacello
- cell_methods :
- area:mean zl:mean yh:mean xh:mean time: mean
- long_name :
- Sea Water Salinity
- standard_name :
- sea_water_salinity
- time_avg_info :
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- units :
- psu
Array Chunk Bytes 18.55 MiB 1.24 MiB Shape (131400, 37) (8760, 37) Dask graph 15 chunks in 6 graph layers Data type float32 numpy.ndarray - taux(time)float32dask.array<chunksize=(131400,), meta=np.ndarray>
- cell_methods :
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- interp_method :
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- long_name :
- Zonal surface stress from ocean interactions with atmos and ice
- standard_name :
- surface_downward_x_stress
- time_avg_info :
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- units :
- Pa
Array Chunk Bytes 513.28 kiB 513.28 kiB Shape (131400,) (131400,) Dask graph 1 chunks in 4 graph layers Data type float32 numpy.ndarray - tauy(time)float32dask.array<chunksize=(131400,), meta=np.ndarray>
- cell_methods :
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- interp_method :
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- long_name :
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- standard_name :
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- time_avg_info :
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- units :
- Pa
Array Chunk Bytes 513.28 kiB 513.28 kiB Shape (131400,) (131400,) Dask graph 1 chunks in 4 graph layers Data type float32 numpy.ndarray - thetao(time, zl)float32dask.array<chunksize=(8760, 37), meta=np.ndarray>
- cell_measures :
- volume: volcello area: areacello
- cell_methods :
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- long_name :
- Sea Water Potential Temperature
- standard_name :
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- time_avg_info :
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- units :
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Array Chunk Bytes 18.55 MiB 1.24 MiB Shape (131400, 37) (8760, 37) Dask graph 15 chunks in 6 graph layers Data type float32 numpy.ndarray - uo(time, zl)float32dask.array<chunksize=(8760, 37), meta=np.ndarray>
- cell_methods :
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- interp_method :
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- long_name :
- Sea Water X Velocity
- standard_name :
- sea_water_x_velocity
- time_avg_info :
- average_T1,average_T2,average_DT
- units :
- m s-1
Array Chunk Bytes 18.55 MiB 1.24 MiB Shape (131400, 37) (8760, 37) Dask graph 15 chunks in 6 graph layers Data type float32 numpy.ndarray - vo(time, zl)float32dask.array<chunksize=(8760, 37), meta=np.ndarray>
- cell_methods :
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- interp_method :
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- long_name :
- Sea Water Y Velocity
- standard_name :
- sea_water_y_velocity
- time_avg_info :
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- units :
- m s-1
Array Chunk Bytes 18.55 MiB 1.24 MiB Shape (131400, 37) (8760, 37) Dask graph 15 chunks in 6 graph layers Data type float32 numpy.ndarray - volcello(time, zl)float32dask.array<chunksize=(8760, 37), meta=np.ndarray>
- cell_methods :
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- long_name :
- Ocean grid-cell volume
- standard_name :
- ocean_volume
- time_avg_info :
- average_T1,average_T2,average_DT
- units :
- m3
Array Chunk Bytes 18.55 MiB 1.24 MiB Shape (131400, 37) (8760, 37) Dask graph 15 chunks in 6 graph layers Data type float32 numpy.ndarray - zos(time)float32dask.array<chunksize=(131400,), meta=np.ndarray>
- cell_measures :
- area: areacello
- cell_methods :
- area:mean yh:mean xh:mean time: mean
- long_name :
- Sea surface height above geoid
- standard_name :
- sea_surface_height_above_geoid
- time_avg_info :
- average_T1,average_T2,average_DT
- units :
- m
Array Chunk Bytes 513.28 kiB 513.28 kiB Shape (131400,) (131400,) Dask graph 1 chunks in 4 graph layers Data type float32 numpy.ndarray - α(time, zl)float32dask.array<chunksize=(8760, 37), meta=np.ndarray>
- standard_name :
- sea_water_thermal_expansion_coefficient
- units :
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Array Chunk Bytes 18.55 MiB 1.24 MiB Shape (131400, 37) (8760, 37) Dask graph 15 chunks in 6 graph layers Data type float32 numpy.ndarray - β(time, zl)float32dask.array<chunksize=(8760, 37), meta=np.ndarray>
- standard_name :
- sea_water_haline_contraction_coefficient
- units :
- kg/g
Array Chunk Bytes 18.55 MiB 1.24 MiB Shape (131400, 37) (8760, 37) Dask graph 15 chunks in 6 graph layers Data type float32 numpy.ndarray - Tz(time, zi)float32dask.array<chunksize=(8760, 36), meta=np.ndarray>
- long_name :
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- units :
- Cm$^{-1}$
Array Chunk Bytes 18.55 MiB 1.20 MiB Shape (131400, 37) (8760, 36) Dask graph 30 chunks in 24 graph layers Data type float32 numpy.ndarray - Sz(time, zi)float32dask.array<chunksize=(8760, 36), meta=np.ndarray>
- long_name :
- $S_z$
- units :
- m$^{-1}$
Array Chunk Bytes 18.55 MiB 1.20 MiB Shape (131400, 37) (8760, 36) Dask graph 30 chunks in 24 graph layers Data type float32 numpy.ndarray - N2T(time, zi)float32dask.array<chunksize=(8760, 36), meta=np.ndarray>
- long_name :
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- units :
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Array Chunk Bytes 18.55 MiB 1.20 MiB Shape (131400, 37) (8760, 36) Dask graph 30 chunks in 28 graph layers Data type float32 numpy.ndarray - S2(time, zi)float32dask.array<chunksize=(8760, 36), meta=np.ndarray>
- long_name :
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- units :
- s$^{-2}$
Array Chunk Bytes 18.55 MiB 1.20 MiB Shape (131400, 37) (8760, 36) Dask graph 30 chunks in 51 graph layers Data type float32 numpy.ndarray - shred2(time, zi)float32dask.array<chunksize=(8760, 36), meta=np.ndarray>
- long_name :
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- units :
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Array Chunk Bytes 18.55 MiB 1.20 MiB Shape (131400, 37) (8760, 36) Dask graph 30 chunks in 67 graph layers Data type float32 numpy.ndarray - Rig_T(time, zi)float32dask.array<chunksize=(8760, 36), meta=np.ndarray>
- long_name :
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Array Chunk Bytes 18.55 MiB 1.20 MiB Shape (131400, 37) (8760, 36) Dask graph 30 chunks in 66 graph layers Data type float32 numpy.ndarray - Rig(time, zi)float32dask.array<chunksize=(8760, 36), meta=np.ndarray>
- cell_measures :
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- cell_methods :
- area:mean zi:point yh:mean xh:mean time: mean
- long_name :
- $Ri^g$
- time_avg_info :
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Array Chunk Bytes 18.55 MiB 1.20 MiB Shape (131400, 37) (8760, 36) Dask graph 30 chunks in 56 graph layers Data type float32 numpy.ndarray - tau(time)float32dask.array<chunksize=(131400,), meta=np.ndarray>
- cell_methods :
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- time_avg_info :
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- units :
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Array Chunk Bytes 513.28 kiB 513.28 kiB Shape (131400,) (131400,) Dask graph 1 chunks in 10 graph layers Data type float32 numpy.ndarray - Jb(time, zi)float32dask.array<chunksize=(8760, 36), meta=np.ndarray>
- cell_measures :
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- cell_methods :
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- long_name :
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- standard_name :
- ocean_vertical_diffusive_buoyancy_flux
- time_avg_info :
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- units :
- m2 s-1
Array Chunk Bytes 18.55 MiB 1.20 MiB Shape (131400, 37) (8760, 36) Dask graph 30 chunks in 63 graph layers Data type float32 numpy.ndarray - Jq(time, zi)float64dask.array<chunksize=(8760, 37), meta=np.ndarray>
- units :
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- long_name :
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Array Chunk Bytes 37.09 MiB 2.47 MiB Shape (131400, 37) (8760, 37) Dask graph 15 chunks in 7 graph layers Data type float64 numpy.ndarray - ν(time, zl)float32dask.array<chunksize=(8760, 37), meta=np.ndarray>
- cell_methods :
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- interp_method :
- none
- long_name :
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- standard_name :
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- time_avg_info :
- average_T1,average_T2,average_DT
- units :
- m2 s-1
Array Chunk Bytes 18.55 MiB 1.24 MiB Shape (131400, 37) (8760, 37) Dask graph 15 chunks in 6 graph layers Data type float32 numpy.ndarray - shear_prod(time, zi)float32dask.array<chunksize=(8760, 36), meta=np.ndarray>
- long_name :
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- units :
- W/kg
Array Chunk Bytes 18.55 MiB 1.20 MiB Shape (131400, 37) (8760, 36) Dask graph 30 chunks in 72 graph layers Data type float32 numpy.ndarray - eps(time, zi)float32dask.array<chunksize=(8760, 36), meta=np.ndarray>
- long_name :
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- units :
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Array Chunk Bytes 18.55 MiB 1.20 MiB Shape (131400, 37) (8760, 36) Dask graph 30 chunks in 122 graph layers Data type float32 numpy.ndarray - chi(time, zi)float32dask.array<chunksize=(8760, 36), meta=np.ndarray>
- long_name :
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- units :
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Array Chunk Bytes 18.55 MiB 1.20 MiB Shape (131400, 37) (8760, 36) Dask graph 30 chunks in 31 graph layers Data type float32 numpy.ndarray - Rif(time, zi)float32dask.array<chunksize=(8760, 36), meta=np.ndarray>
- cell_measures :
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- cell_methods :
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- long_name :
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- units :
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Array Chunk Bytes 18.55 MiB 1.20 MiB Shape (131400, 37) (8760, 36) Dask graph 30 chunks in 123 graph layers Data type float32 numpy.ndarray - sst(time)float32dask.array<chunksize=(8760,), meta=np.ndarray>
- cell_measures :
- volume: volcello area: areacello
- cell_methods :
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- long_name :
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- standard_name :
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- time_avg_info :
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- units :
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Array Chunk Bytes 513.28 kiB 34.22 kiB Shape (131400,) (8760,) Dask graph 15 chunks in 6 graph layers Data type float32 numpy.ndarray
- title :
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<xarray.DatasetView> Dimensions: (time: 131400, zl: 37, zi: 37, nv: 2) Coordinates: (12/16) * nv (nv) float64 1.0 2.0 * time (time) datetime64[ns] 2003-01-01T00:30:00... xh float64 -140.0 yh float64 0.0625 yq float64 -0.0625 * zi (zi) float64 -523.8 -481.0 ... -2.5 -0.0 ... ... oni (time) float32 0.92 0.92 ... -0.87 -0.87 en_mask (time) bool True True True ... False False ln_mask (time) bool False False False ... True True warm_mask (time) bool False False False ... True True cool_mask (time) bool True True True ... False False enso_transition (time) <U12 'El-Nino cool' ... 'La-Nina w... Data variables: (12/58) KPP_BulkRi (time, zl) float32 dask.array<chunksize=(8760, 37), meta=np.ndarray> KPP_N2 (time, zi) float32 dask.array<chunksize=(8760, 37), meta=np.ndarray> KPP_NLT_temp_budget (time, zl) float32 dask.array<chunksize=(8760, 37), meta=np.ndarray> KPP_NLtransport_heat (time, zi) float32 dask.array<chunksize=(8760, 37), meta=np.ndarray> KPP_OBLdepth (time) float32 dask.array<chunksize=(131400,), meta=np.ndarray> KPP_buoyFlux (time, zi) float32 dask.array<chunksize=(8760, 37), meta=np.ndarray> ... ... ν (time, zl) float32 dask.array<chunksize=(8760, 37), meta=np.ndarray> shear_prod (time, zi) float32 dask.array<chunksize=(8760, 36), meta=np.ndarray> eps (time, zi) float32 dask.array<chunksize=(8760, 36), meta=np.ndarray> chi (time, zi) float32 dask.array<chunksize=(8760, 36), meta=np.ndarray> Rif (time, zi) float32 dask.array<chunksize=(8760, 36), meta=np.ndarray> sst (time) float32 dask.array<chunksize=(8760,), meta=np.ndarray> Attributes: title: baselinebaseline.hb- time: 130968
- zi: 37
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- nv: 2
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array([1., 2.])
- time(time)datetime64[ns]2003-01-07T00:30:00 ... 2017-12-...
array(['2003-01-07T00:30:00.000000000', '2003-01-07T01:30:00.000000000', '2003-01-07T02:30:00.000000000', ..., '2017-12-31T21:30:00.000000000', '2017-12-31T22:30:00.000000000', '2017-12-31T23:30:00.000000000'], dtype='datetime64[ns]') - xh()float64-140.0
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- domain_decomposition :
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- units :
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array(-140.)
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- cartesian_axis :
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- domain_decomposition :
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- units :
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array(0.06249997)
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- domain_decomposition :
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- long_name :
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- units :
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array(-0.06249997)
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- cartesian_axis :
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- long_name :
- Interface pseudo-depth, -z*
- positive :
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- units :
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array([-523.8 , -481.01, -442.51, -407.64, -375.88, -346.78, -319.99, -295.22, -272.22, -250.8 , -230.78, -212.02, -194.41, -177.85, -162.26, -147.57, -133.72, -120.66, -108.37, -96.83, -86.02, -75.94, -66.57, -57.91, -49.94, -42.66, -36.05, -30.1 , -24.81, -20.16, -16.15, -12.77, -10. , -7.5 , -5. , -2.5 , -0. ]) - zl(zl)float64-547.8 -502.4 ... -3.75 -1.25
- cartesian_axis :
- Z
- long_name :
- Layer pseudo-depth, -z*
- positive :
- up
- units :
- meter
array([-547.75 , -502.405, -461.76 , -425.075, -391.76 , -361.33 , -333.385, -307.605, -283.72 , -261.51 , -240.79 , -221.4 , -203.215, -186.13 , -170.055, -154.915, -140.645, -127.19 , -114.515, -102.6 , -91.425, -80.98 , -71.255, -62.24 , -53.925, -46.3 , -39.355, -33.075, -27.455, -22.485, -18.155, -14.46 , -11.385, -8.75 , -6.25 , -3.75 , -1.25 ]) - eucmax(time)float64dask.array<chunksize=(8760,), meta=np.ndarray>
- units :
- m
- long_name :
- EUC maximum
- positive :
- up
Array Chunk Bytes 1.00 MiB 68.44 kiB Shape (130968,) (8760,) Dask graph 15 chunks in 22 graph layers Data type float64 numpy.ndarray - mldT(time)float64dask.array<chunksize=(8760,), meta=np.ndarray>
- long_name :
- MLD$_θ$
- units :
- m
- description :
- Interpolate θi to 1m grid. Search for max depth where |dθ| > 0.15
Array Chunk Bytes 1.00 MiB 68.44 kiB Shape (130968,) (8760,) Dask graph 15 chunks in 24 graph layers Data type float64 numpy.ndarray - dcl_mask(zi, time)booldask.array<chunksize=(37, 8760), meta=np.ndarray>
- description :
- True when 5m below mldT and above eucmax.
Array Chunk Bytes 4.62 MiB 316.52 kiB Shape (37, 130968) (37, 8760) Dask graph 15 chunks in 57 graph layers Data type bool numpy.ndarray - oni(time)float320.63 0.63 0.63 ... -0.87 -0.87
array([ 0.63, 0.63, 0.63, ..., -0.87, -0.87, -0.87], dtype=float32)
- en_mask(time)boolFalse False False ... False False
array([False, False, False, ..., False, False, False])
- ln_mask(time)boolFalse False False ... True True
array([False, False, False, ..., True, True, True])
- warm_mask(time)boolTrue True True ... True True True
array([ True, True, True, ..., True, True, True])
- cool_mask(time)boolFalse False False ... False False
array([False, False, False, ..., False, False, False])
- enso_transition(time)<U12'____________' ... 'La-Nina warm'
- description :
- Warner & Moum (2019) ENSO transition phase; El-Nino = ONI > 0.5 for at least 6 months; La-Nina = ONI < -0.5 for at least 6 months
array(['____________', '____________', '____________', ..., 'La-Nina warm', 'La-Nina warm', 'La-Nina warm'], dtype='<U12')
- KPP_N2(time, zi)float32dask.array<chunksize=(8760, 37), meta=np.ndarray>
- cell_measures :
- area: areacello
- cell_methods :
- area:mean zi:point yh:mean xh:mean time: mean
- long_name :
- Square of Brunt-Vaisala frequency used by [CVMix] KPP
- time_avg_info :
- average_T1,average_T2,average_DT
- units :
- 1/s2
Array Chunk Bytes 18.49 MiB 1.24 MiB Shape (130968, 37) (8760, 37) Dask graph 15 chunks in 6 graph layers Data type float32 numpy.ndarray - KPP_NLT_temp_budget(time, zl)float32dask.array<chunksize=(8760, 37), meta=np.ndarray>
- cell_measures :
- volume: volcello area: areacello
- cell_methods :
- area:mean zl:sum yh:mean xh:mean time: mean
- long_name :
- Heat content change due to non-local transport, as calculated by [CVMix] KPP
- time_avg_info :
- average_T1,average_T2,average_DT
- units :
- W m-2
Array Chunk Bytes 18.49 MiB 1.24 MiB Shape (130968, 37) (8760, 37) Dask graph 15 chunks in 6 graph layers Data type float32 numpy.ndarray - KPP_NLtransport_heat(time, zi)float32dask.array<chunksize=(8760, 37), meta=np.ndarray>
- cell_measures :
- area: areacello
- cell_methods :
- area:mean zi:point yh:mean xh:mean time: mean
- long_name :
- Non-local transport (Cs*G(sigma)) for heat, as calculated by [CVMix] KPP
- time_avg_info :
- average_T1,average_T2,average_DT
- units :
- nondim
Array Chunk Bytes 18.49 MiB 1.24 MiB Shape (130968, 37) (8760, 37) Dask graph 15 chunks in 6 graph layers Data type float32 numpy.ndarray - KPP_OBLdepth(time)float32dask.array<chunksize=(130968,), meta=np.ndarray>
- cell_measures :
- area: areacello
- cell_methods :
- area:mean yh:mean xh:mean time: mean
- long_name :
- Thickness of the surface Ocean Boundary Layer calculated by [CVMix] KPP
- time_avg_info :
- average_T1,average_T2,average_DT
- units :
- meter
Array Chunk Bytes 511.59 kiB 511.59 kiB Shape (130968,) (130968,) Dask graph 1 chunks in 4 graph layers Data type float32 numpy.ndarray - KPP_buoyFlux(time, zi)float32dask.array<chunksize=(8760, 37), meta=np.ndarray>
- cell_measures :
- area: areacello
- cell_methods :
- area:mean zi:point yh:mean xh:mean time: mean
- long_name :
- Surface (and penetrating) buoyancy flux, as used by [CVMix] KPP
- time_avg_info :
- average_T1,average_T2,average_DT
- units :
- m2/s3
Array Chunk Bytes 18.49 MiB 1.24 MiB Shape (130968, 37) (8760, 37) Dask graph 15 chunks in 6 graph layers Data type float32 numpy.ndarray - KPP_kheat(time, zi)float32dask.array<chunksize=(8760, 37), meta=np.ndarray>
- cell_measures :
- area: areacello
- cell_methods :
- area:mean zi:point yh:mean xh:mean time: mean
- long_name :
- Heat diffusivity due to KPP, as calculated by [CVMix] KPP
- time_avg_info :
- average_T1,average_T2,average_DT
- units :
- m2/s
Array Chunk Bytes 18.49 MiB 1.24 MiB Shape (130968, 37) (8760, 37) Dask graph 15 chunks in 6 graph layers Data type float32 numpy.ndarray - Kd_heat(time, zi)float32dask.array<chunksize=(8760, 37), meta=np.ndarray>
- cell_measures :
- area: areacello
- cell_methods :
- area:mean zi:point yh:mean xh:mean time: mean
- long_name :
- Total diapycnal diffusivity for heat at interfaces
- standard_name :
- ocean_vertical_heat_diffusivity
- time_avg_info :
- average_T1,average_T2,average_DT
- units :
- m2 s-1
Array Chunk Bytes 18.49 MiB 1.24 MiB Shape (130968, 37) (8760, 37) Dask graph 15 chunks in 6 graph layers Data type float32 numpy.ndarray - Kv_u(time, zl)float32dask.array<chunksize=(8760, 37), meta=np.ndarray>
- cell_methods :
- zl:mean yh:mean xq:point time: mean
- interp_method :
- none
- long_name :
- Total vertical viscosity at u-points
- standard_name :
- ocean_vertical_x_viscosity
- time_avg_info :
- average_T1,average_T2,average_DT
- units :
- m2 s-1
Array Chunk Bytes 18.49 MiB 1.24 MiB Shape (130968, 37) (8760, 37) Dask graph 15 chunks in 6 graph layers Data type float32 numpy.ndarray - Kv_v(time, zl)float32dask.array<chunksize=(8760, 37), meta=np.ndarray>
- cell_methods :
- zl:mean yq:point xh:mean time: mean
- interp_method :
- none
- long_name :
- Total vertical viscosity at v-points
- standard_name :
- ocean_vertical_y_viscosity
- time_avg_info :
- average_T1,average_T2,average_DT
- units :
- m2 s-1
Array Chunk Bytes 18.49 MiB 1.24 MiB Shape (130968, 37) (8760, 37) Dask graph 15 chunks in 6 graph layers Data type float32 numpy.ndarray - N2(time, zi)float32dask.array<chunksize=(8760, 37), meta=np.ndarray>
- cell_measures :
- area: areacello
- cell_methods :
- area:mean zi:point yh:mean xh:mean time: mean
- long_name :
- Buoyancy frequency squared
- time_avg_info :
- average_T1,average_T2,average_DT
- units :
- s-2
Array Chunk Bytes 18.49 MiB 1.24 MiB Shape (130968, 37) (8760, 37) Dask graph 15 chunks in 6 graph layers Data type float32 numpy.ndarray - N2_shear(time, zi)float32dask.array<chunksize=(8760, 37), meta=np.ndarray>
- cell_measures :
- area: areacello
- cell_methods :
- area:mean zi:point yh:mean xh:mean time: mean
- long_name :
- Square of Brunt-Vaisala frequency used by MOM_CVMix_shear module
- time_avg_info :
- average_T1,average_T2,average_DT
- units :
- 1/s2
Array Chunk Bytes 18.49 MiB 1.24 MiB Shape (130968, 37) (8760, 37) Dask graph 15 chunks in 6 graph layers Data type float32 numpy.ndarray - S2_shear(time, zi)float32dask.array<chunksize=(8760, 37), meta=np.ndarray>
- cell_measures :
- area: areacello
- cell_methods :
- area:mean zi:point yh:mean xh:mean time: mean
- long_name :
- Square of vertical shear used by MOM_CVMix_shear module
- time_avg_info :
- average_T1,average_T2,average_DT
- units :
- 1/s2
Array Chunk Bytes 18.49 MiB 1.24 MiB Shape (130968, 37) (8760, 37) Dask graph 15 chunks in 6 graph layers Data type float32 numpy.ndarray - SW(time)float32dask.array<chunksize=(130968,), meta=np.ndarray>
- cell_measures :
- area: areacello
- cell_methods :
- area:mean yh:mean xh:mean time: mean
- long_name :
- Shortwave radiation flux into ocean
- standard_name :
- net_downward_shortwave_flux_at_sea_water_surface
- time_avg_info :
- average_T1,average_T2,average_DT
- units :
- W m-2
Array Chunk Bytes 511.59 kiB 511.59 kiB Shape (130968,) (130968,) Dask graph 1 chunks in 4 graph layers Data type float32 numpy.ndarray - T_advection_xy(time, zl)float32dask.array<chunksize=(8760, 37), meta=np.ndarray>
- cell_measures :
- volume: volcello area: areacello
- cell_methods :
- area:mean zl:sum yh:mean xh:mean time: mean
- long_name :
- Horizontal convergence of residual mean advective fluxes of heat
- time_avg_info :
- average_T1,average_T2,average_DT
- units :
- W m-2
Array Chunk Bytes 18.49 MiB 1.24 MiB Shape (130968, 37) (8760, 37) Dask graph 15 chunks in 6 graph layers Data type float32 numpy.ndarray - T_lbdxy_cont_tendency(time, zl)float32dask.array<chunksize=(8760, 37), meta=np.ndarray>
- cell_measures :
- volume: volcello area: areacello
- cell_methods :
- area:sum zl:sum yh:sum xh:sum time: mean
- long_name :
- Lateral diffusion tracer content tendency for T
- time_avg_info :
- average_T1,average_T2,average_DT
- units :
- W m-2
Array Chunk Bytes 18.49 MiB 1.24 MiB Shape (130968, 37) (8760, 37) Dask graph 15 chunks in 6 graph layers Data type float32 numpy.ndarray - Tflx_dia_diff(time, zi)float32dask.array<chunksize=(8760, 37), meta=np.ndarray>
- cell_measures :
- area: areacello
- cell_methods :
- area:mean zi:point yh:mean xh:mean time: mean
- long_name :
- Diffusive diapycnal temperature flux across interfaces
- time_avg_info :
- average_T1,average_T2,average_DT
- units :
- degC m s-1
- standard_name :
- ocean_vertical_diffusive_heat_flux
Array Chunk Bytes 18.49 MiB 1.24 MiB Shape (130968, 37) (8760, 37) Dask graph 15 chunks in 6 graph layers Data type float32 numpy.ndarray - Th_tendency_vert_remap(time, zl)float32dask.array<chunksize=(8760, 37), meta=np.ndarray>
- cell_measures :
- volume: volcello area: areacello
- cell_methods :
- area:mean zl:sum yh:mean xh:mean time: mean
- long_name :
- Vertical remapping tracer content tendency for Heat
- time_avg_info :
- average_T1,average_T2,average_DT
- units :
- W m-2
Array Chunk Bytes 18.49 MiB 1.24 MiB Shape (130968, 37) (8760, 37) Dask graph 15 chunks in 6 graph layers Data type float32 numpy.ndarray - boundary_forcing_heat_tendency(time, zl)float32dask.array<chunksize=(8760, 37), meta=np.ndarray>
- cell_measures :
- volume: volcello area: areacello
- cell_methods :
- area:mean zl:sum yh:mean xh:mean time: mean
- long_name :
- Boundary forcing heat tendency
- time_avg_info :
- average_T1,average_T2,average_DT
- units :
- W m-2
Array Chunk Bytes 18.49 MiB 1.24 MiB Shape (130968, 37) (8760, 37) Dask graph 15 chunks in 6 graph layers Data type float32 numpy.ndarray - dens(time, zl)float32dask.array<chunksize=(8760, 37), meta=np.ndarray>
- cell_measures :
- volume: volcello area: areacello
- cell_methods :
- area:mean zl:mean yh:mean xh:mean time: mean
- long_name :
- Sea Water Salinity
- standard_name :
- sea_water_potential_density
- time_avg_info :
- average_T1,average_T2,average_DT
- units :
- kg/m^3
Array Chunk Bytes 18.49 MiB 1.24 MiB Shape (130968, 37) (8760, 37) Dask graph 15 chunks in 6 graph layers Data type float32 numpy.ndarray - densT(time, zl)float32dask.array<chunksize=(8760, 37), meta=np.ndarray>
- cell_measures :
- volume: volcello area: areacello
- cell_methods :
- area:mean zl:mean yh:mean xh:mean time: mean
- long_name :
- Sea Water Potential Temperature
- standard_name :
- sea_water_potential_density
- time_avg_info :
- average_T1,average_T2,average_DT
- units :
- kg/m3
Array Chunk Bytes 18.49 MiB 1.24 MiB Shape (130968, 37) (8760, 37) Dask graph 15 chunks in 6 graph layers Data type float32 numpy.ndarray - frazil_heat_tendency(time, zl)float32dask.array<chunksize=(8760, 37), meta=np.ndarray>
- cell_measures :
- volume: volcello area: areacello
- cell_methods :
- area:mean zl:sum yh:mean xh:mean time: mean
- long_name :
- Heat tendency due to frazil formation
- time_avg_info :
- average_T1,average_T2,average_DT
- units :
- W m-2
Array Chunk Bytes 18.49 MiB 1.24 MiB Shape (130968, 37) (8760, 37) Dask graph 15 chunks in 6 graph layers Data type float32 numpy.ndarray - h(time, zl)float32dask.array<chunksize=(8760, 37), meta=np.ndarray>
- cell_measures :
- volume: volcello area: areacello
- cell_methods :
- area:mean zl:sum yh:mean xh:mean time: mean
- long_name :
- Layer Thickness
- time_avg_info :
- average_T1,average_T2,average_DT
- units :
- m
Array Chunk Bytes 18.49 MiB 1.24 MiB Shape (130968, 37) (8760, 37) Dask graph 15 chunks in 6 graph layers Data type float32 numpy.ndarray - mlotst(time)float32dask.array<chunksize=(130968,), meta=np.ndarray>
- cell_measures :
- area: areacello
- cell_methods :
- area:mean yh:mean xh:mean time: mean
- long_name :
- Ocean Mixed Layer Thickness Defined by Sigma T
- standard_name :
- ocean_mixed_layer_thickness_defined_by_sigma_t
- time_avg_info :
- average_T1,average_T2,average_DT
- units :
- m
Array Chunk Bytes 511.59 kiB 511.59 kiB Shape (130968,) (130968,) Dask graph 1 chunks in 4 graph layers Data type float32 numpy.ndarray - net_heat_surface(time)float32dask.array<chunksize=(130968,), meta=np.ndarray>
- cell_measures :
- area: areacello
- cell_methods :
- area:mean yh:mean xh:mean time: mean
- long_name :
- Surface ocean heat flux from SW+LW+lat+sens+mass transfer+frazil+restore+seaice_melt_heat or flux adjustments
- standard_name :
- surface_downward_heat_flux_in_sea_water
- time_avg_info :
- average_T1,average_T2,average_DT
- units :
- W m-2
Array Chunk Bytes 511.59 kiB 511.59 kiB Shape (130968,) (130968,) Dask graph 1 chunks in 4 graph layers Data type float32 numpy.ndarray - opottempdiff(time, zl)float32dask.array<chunksize=(8760, 37), meta=np.ndarray>
- cell_measures :
- volume: volcello area: areacello
- cell_methods :
- area:mean zl:sum yh:mean xh:mean time: mean
- long_name :
- Tendency of sea water potential temperature expressed as heat content due to parameterized dianeutral mixing
- standard_name :
- tendency_of_sea_water_potential_temperature_expressed_as_heat_content_due_to_parameterized_dianeutral_mixing
- time_avg_info :
- average_T1,average_T2,average_DT
- units :
- W m-2
Array Chunk Bytes 18.49 MiB 1.24 MiB Shape (130968, 37) (8760, 37) Dask graph 15 chunks in 6 graph layers Data type float32 numpy.ndarray - opottemppmdiff(time, zl)float32dask.array<chunksize=(8760, 37), meta=np.ndarray>
- cell_measures :
- volume: volcello area: areacello
- cell_methods :
- area:sum zl:sum yh:sum xh:sum time: mean
- long_name :
- Tendency of sea water potential temperature expressed as heat content due to parameterized mesoscale neutral diffusion
- standard_name :
- tendency_of_sea_water_potential_temperature_expressed_as_heat_content_due_to_parameterized_mesoscale_neutral_diffusion
- time_avg_info :
- average_T1,average_T2,average_DT
- units :
- W m-2
Array Chunk Bytes 18.49 MiB 1.24 MiB Shape (130968, 37) (8760, 37) Dask graph 15 chunks in 6 graph layers Data type float32 numpy.ndarray - opottemptend(time, zl)float32dask.array<chunksize=(8760, 37), meta=np.ndarray>
- cell_measures :
- volume: volcello area: areacello
- cell_methods :
- area:mean zl:sum yh:mean xh:mean time: mean
- long_name :
- Tendency of Sea Water Potential Temperature Expressed as Heat Content
- standard_name :
- tendency_of_sea_water_potential_temperature_expressed_as_heat_content
- time_avg_info :
- average_T1,average_T2,average_DT
- units :
- W m-2
Array Chunk Bytes 18.49 MiB 1.24 MiB Shape (130968, 37) (8760, 37) Dask graph 15 chunks in 6 graph layers Data type float32 numpy.ndarray - ri_grad_shear(time, zi)float32dask.array<chunksize=(8760, 37), meta=np.ndarray>
- cell_measures :
- area: areacello
- cell_methods :
- area:mean zi:point yh:mean xh:mean time: mean
- long_name :
- Gradient Richarson number used by MOM_CVMix_shear module
- time_avg_info :
- average_T1,average_T2,average_DT
- units :
- nondim
Array Chunk Bytes 18.49 MiB 1.24 MiB Shape (130968, 37) (8760, 37) Dask graph 15 chunks in 6 graph layers Data type float32 numpy.ndarray - so(time, zl)float32dask.array<chunksize=(8760, 37), meta=np.ndarray>
- cell_measures :
- volume: volcello area: areacello
- cell_methods :
- area:mean zl:mean yh:mean xh:mean time: mean
- long_name :
- Sea Water Salinity
- standard_name :
- sea_water_salinity
- time_avg_info :
- average_T1,average_T2,average_DT
- units :
- psu
Array Chunk Bytes 18.49 MiB 1.24 MiB Shape (130968, 37) (8760, 37) Dask graph 15 chunks in 6 graph layers Data type float32 numpy.ndarray - taux(time)float32dask.array<chunksize=(130968,), meta=np.ndarray>
- cell_methods :
- yh:mean xq:point time: mean
- interp_method :
- none
- long_name :
- Zonal surface stress from ocean interactions with atmos and ice
- standard_name :
- surface_downward_x_stress
- time_avg_info :
- average_T1,average_T2,average_DT
- units :
- Pa
Array Chunk Bytes 511.59 kiB 511.59 kiB Shape (130968,) (130968,) Dask graph 1 chunks in 4 graph layers Data type float32 numpy.ndarray - tauy(time)float32dask.array<chunksize=(130968,), meta=np.ndarray>
- cell_methods :
- yq:point xh:mean time: mean
- interp_method :
- none
- long_name :
- Meridional surface stress ocean interactions with atmos and ice
- standard_name :
- surface_downward_y_stress
- time_avg_info :
- average_T1,average_T2,average_DT
- units :
- Pa
Array Chunk Bytes 511.59 kiB 511.59 kiB Shape (130968,) (130968,) Dask graph 1 chunks in 4 graph layers Data type float32 numpy.ndarray - thetao(time, zl)float32dask.array<chunksize=(8760, 37), meta=np.ndarray>
- cell_measures :
- volume: volcello area: areacello
- cell_methods :
- area:mean zl:mean yh:mean xh:mean time: mean
- long_name :
- Sea Water Potential Temperature
- standard_name :
- sea_water_potential_temperature
- time_avg_info :
- average_T1,average_T2,average_DT
- units :
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Array Chunk Bytes 18.49 MiB 1.24 MiB Shape (130968, 37) (8760, 37) Dask graph 15 chunks in 6 graph layers Data type float32 numpy.ndarray - uo(time, zl)float32dask.array<chunksize=(8760, 37), meta=np.ndarray>
- cell_methods :
- zl:mean yh:mean xq:point time: mean
- interp_method :
- none
- long_name :
- Sea Water X Velocity
- standard_name :
- sea_water_x_velocity
- time_avg_info :
- average_T1,average_T2,average_DT
- units :
- m s-1
Array Chunk Bytes 18.49 MiB 1.24 MiB Shape (130968, 37) (8760, 37) Dask graph 15 chunks in 6 graph layers Data type float32 numpy.ndarray - vo(time, zl)float32dask.array<chunksize=(8760, 37), meta=np.ndarray>
- cell_methods :
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- interp_method :
- none
- long_name :
- Sea Water Y Velocity
- standard_name :
- sea_water_y_velocity
- time_avg_info :
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- units :
- m s-1
Array Chunk Bytes 18.49 MiB 1.24 MiB Shape (130968, 37) (8760, 37) Dask graph 15 chunks in 6 graph layers Data type float32 numpy.ndarray - volcello(time, zl)float32dask.array<chunksize=(8760, 37), meta=np.ndarray>
- cell_methods :
- area:sum zl:sum yh:sum xh:sum time: mean
- long_name :
- Ocean grid-cell volume
- standard_name :
- ocean_volume
- time_avg_info :
- average_T1,average_T2,average_DT
- units :
- m3
Array Chunk Bytes 18.49 MiB 1.24 MiB Shape (130968, 37) (8760, 37) Dask graph 15 chunks in 6 graph layers Data type float32 numpy.ndarray - zos(time)float32dask.array<chunksize=(130968,), meta=np.ndarray>
- cell_measures :
- area: areacello
- cell_methods :
- area:mean yh:mean xh:mean time: mean
- long_name :
- Sea surface height above geoid
- standard_name :
- sea_surface_height_above_geoid
- time_avg_info :
- average_T1,average_T2,average_DT
- units :
- m
Array Chunk Bytes 511.59 kiB 511.59 kiB Shape (130968,) (130968,) Dask graph 1 chunks in 4 graph layers Data type float32 numpy.ndarray - α(time, zl)float32dask.array<chunksize=(8760, 37), meta=np.ndarray>
- standard_name :
- sea_water_thermal_expansion_coefficient
- units :
- C-1
Array Chunk Bytes 18.49 MiB 1.24 MiB Shape (130968, 37) (8760, 37) Dask graph 15 chunks in 6 graph layers Data type float32 numpy.ndarray - β(time, zl)float32dask.array<chunksize=(8760, 37), meta=np.ndarray>
- standard_name :
- sea_water_haline_contraction_coefficient
- units :
- kg/g
Array Chunk Bytes 18.49 MiB 1.24 MiB Shape (130968, 37) (8760, 37) Dask graph 15 chunks in 6 graph layers Data type float32 numpy.ndarray - Tz(time, zi)float32dask.array<chunksize=(8760, 36), meta=np.ndarray>
- long_name :
- $T_z$
- units :
- Cm$^{-1}$
Array Chunk Bytes 18.49 MiB 1.20 MiB Shape (130968, 37) (8760, 36) Dask graph 30 chunks in 24 graph layers Data type float32 numpy.ndarray - Sz(time, zi)float32dask.array<chunksize=(8760, 36), meta=np.ndarray>
- long_name :
- $S_z$
- units :
- m$^{-1}$
Array Chunk Bytes 18.49 MiB 1.20 MiB Shape (130968, 37) (8760, 36) Dask graph 30 chunks in 24 graph layers Data type float32 numpy.ndarray - N2T(time, zi)float32dask.array<chunksize=(8760, 36), meta=np.ndarray>
- long_name :
- $N_T^2$
- units :
- s$^{-2}$
Array Chunk Bytes 18.49 MiB 1.20 MiB Shape (130968, 37) (8760, 36) Dask graph 30 chunks in 28 graph layers Data type float32 numpy.ndarray - S2(time, zi)float32dask.array<chunksize=(8760, 36), meta=np.ndarray>
- long_name :
- $S^2$
- units :
- s$^{-2}$
Array Chunk Bytes 18.49 MiB 1.20 MiB Shape (130968, 37) (8760, 36) Dask graph 30 chunks in 51 graph layers Data type float32 numpy.ndarray - shred2(time, zi)float32dask.array<chunksize=(8760, 36), meta=np.ndarray>
- long_name :
- $Sh_{red}^2$
- units :
- $s^{-2}$
Array Chunk Bytes 18.49 MiB 1.20 MiB Shape (130968, 37) (8760, 36) Dask graph 30 chunks in 67 graph layers Data type float32 numpy.ndarray - Rig_T(time, zi)float32dask.array<chunksize=(8760, 36), meta=np.ndarray>
- long_name :
- $Ri^g_T$
Array Chunk Bytes 18.49 MiB 1.20 MiB Shape (130968, 37) (8760, 36) Dask graph 30 chunks in 66 graph layers Data type float32 numpy.ndarray - Rig(time, zi)float32dask.array<chunksize=(8760, 36), meta=np.ndarray>
- cell_measures :
- area: areacello
- cell_methods :
- area:mean zi:point yh:mean xh:mean time: mean
- long_name :
- $Ri^g$
- time_avg_info :
- average_T1,average_T2,average_DT
Array Chunk Bytes 18.49 MiB 1.20 MiB Shape (130968, 37) (8760, 36) Dask graph 30 chunks in 56 graph layers Data type float32 numpy.ndarray - tau(time)float32dask.array<chunksize=(130968,), meta=np.ndarray>
- cell_methods :
- yh:mean xq:point time: mean
- interp_method :
- none
- long_name :
- Zonal surface stress from ocean interactions with atmos and ice
- time_avg_info :
- average_T1,average_T2,average_DT
- units :
- Pa
Array Chunk Bytes 511.59 kiB 511.59 kiB Shape (130968,) (130968,) Dask graph 1 chunks in 10 graph layers Data type float32 numpy.ndarray - Jb(time, zi)float32dask.array<chunksize=(8760, 36), meta=np.ndarray>
- cell_measures :
- area: areacello
- cell_methods :
- area:mean zi:point yh:mean xh:mean time: mean
- long_name :
- Total diapycnal diffusivity for heat at interfaces
- standard_name :
- ocean_vertical_diffusive_buoyancy_flux
- time_avg_info :
- average_T1,average_T2,average_DT
- units :
- m2 s-1
Array Chunk Bytes 18.49 MiB 1.20 MiB Shape (130968, 37) (8760, 36) Dask graph 30 chunks in 63 graph layers Data type float32 numpy.ndarray - Jq(time, zi)float64dask.array<chunksize=(8760, 37), meta=np.ndarray>
- units :
- W/m^2
- long_name :
- $J_q^t$
Array Chunk Bytes 36.97 MiB 2.47 MiB Shape (130968, 37) (8760, 37) Dask graph 15 chunks in 7 graph layers Data type float64 numpy.ndarray - ν(time, zl)float32dask.array<chunksize=(8760, 37), meta=np.ndarray>
- cell_methods :
- zl:mean yh:mean xq:point time: mean
- interp_method :
- none
- long_name :
- Total vertical viscosity at u-points
- standard_name :
- ocean_vertical_momentum_diffusivity
- time_avg_info :
- average_T1,average_T2,average_DT
- units :
- m2 s-1
Array Chunk Bytes 18.49 MiB 1.24 MiB Shape (130968, 37) (8760, 37) Dask graph 15 chunks in 6 graph layers Data type float32 numpy.ndarray - shear_prod(time, zi)float32dask.array<chunksize=(8760, 36), meta=np.ndarray>
- long_name :
- $SP$
- units :
- W/kg
Array Chunk Bytes 18.49 MiB 1.20 MiB Shape (130968, 37) (8760, 36) Dask graph 30 chunks in 72 graph layers Data type float32 numpy.ndarray - eps(time, zi)float32dask.array<chunksize=(8760, 36), meta=np.ndarray>
- long_name :
- $ε$
- units :
- W/kg
Array Chunk Bytes 18.49 MiB 1.20 MiB Shape (130968, 37) (8760, 36) Dask graph 30 chunks in 122 graph layers Data type float32 numpy.ndarray - chi(time, zi)float32dask.array<chunksize=(8760, 36), meta=np.ndarray>
- long_name :
- $χ$
- units :
- C^2/s
Array Chunk Bytes 18.49 MiB 1.20 MiB Shape (130968, 37) (8760, 36) Dask graph 30 chunks in 31 graph layers Data type float32 numpy.ndarray - Rif(time, zi)float32dask.array<chunksize=(8760, 36), meta=np.ndarray>
- cell_measures :
- area: areacello
- cell_methods :
- area:mean zi:point yh:mean xh:mean time: mean
- long_name :
- Total diapycnal diffusivity for heat at interfaces
- standard_name :
- flux_richardson_number
- time_avg_info :
- average_T1,average_T2,average_DT
- units :
- m2 s-1
Array Chunk Bytes 18.49 MiB 1.20 MiB Shape (130968, 37) (8760, 36) Dask graph 30 chunks in 123 graph layers Data type float32 numpy.ndarray - sst(time)float32dask.array<chunksize=(8760,), meta=np.ndarray>
- cell_measures :
- volume: volcello area: areacello
- cell_methods :
- area:mean zl:mean yh:mean xh:mean time: mean
- long_name :
- $SST$
- standard_name :
- sea_surface_temperature
- time_avg_info :
- average_T1,average_T2,average_DT
- units :
- degC
Array Chunk Bytes 511.59 kiB 34.22 kiB Shape (130968,) (8760,) Dask graph 15 chunks in 6 graph layers Data type float32 numpy.ndarray
- title :
- KPP ν0=2.5, Ric=0.2, Ri0=0.5
<xarray.DatasetView> Dimensions: (time: 130968, zi: 37, zl: 37, nv: 2) Coordinates: (12/16) * nv (nv) float64 1.0 2.0 * time (time) datetime64[ns] 2003-01-07T00:30:00... xh float64 -140.0 yh float64 0.0625 yq float64 -0.0625 * zi (zi) float64 -523.8 -481.0 ... -2.5 -0.0 ... ... oni (time) float32 0.63 0.63 ... -0.87 -0.87 en_mask (time) bool False False ... False False ln_mask (time) bool False False False ... True True warm_mask (time) bool True True True ... True True cool_mask (time) bool False False ... False False enso_transition (time) <U12 '____________' ... 'La-Nina w... Data variables: (12/54) KPP_N2 (time, zi) float32 dask.array<chunksize=(8760, 37), meta=np.ndarray> KPP_NLT_temp_budget (time, zl) float32 dask.array<chunksize=(8760, 37), meta=np.ndarray> KPP_NLtransport_heat (time, zi) float32 dask.array<chunksize=(8760, 37), meta=np.ndarray> KPP_OBLdepth (time) float32 dask.array<chunksize=(130968,), meta=np.ndarray> KPP_buoyFlux (time, zi) float32 dask.array<chunksize=(8760, 37), meta=np.ndarray> KPP_kheat (time, zi) float32 dask.array<chunksize=(8760, 37), meta=np.ndarray> ... ... ν (time, zl) float32 dask.array<chunksize=(8760, 37), meta=np.ndarray> shear_prod (time, zi) float32 dask.array<chunksize=(8760, 36), meta=np.ndarray> eps (time, zi) float32 dask.array<chunksize=(8760, 36), meta=np.ndarray> chi (time, zi) float32 dask.array<chunksize=(8760, 36), meta=np.ndarray> Rif (time, zi) float32 dask.array<chunksize=(8760, 36), meta=np.ndarray> sst (time) float32 dask.array<chunksize=(8760,), meta=np.ndarray> Attributes: title: KPP ν0=2.5, Ric=0.2, Ri0=0.5baseline.kpp.lmd.004- time: 131400
- zl: 37
- zi: 37
- nv: 2
- nv(nv)float641.0 2.0
- long_name :
- vertex number
array([1., 2.])
- time(time)datetime64[ns]2003-01-01T00:30:00 ... 2017-12-...
array(['2003-01-01T00:30:00.000000000', '2003-01-01T01:30:00.000000000', '2003-01-01T02:30:00.000000000', ..., '2017-12-31T21:30:00.000000000', '2017-12-31T22:30:00.000000000', '2017-12-31T23:30:00.000000000'], dtype='datetime64[ns]') - xh()float64-140.0
- axis :
- X
- domain_decomposition :
- [220, 222, 220, 221]
- long_name :
- h point nominal longitude
- units :
- degrees_east
array(-140.)
- yh()float640.0625
- axis :
- Y
- domain_decomposition :
- [210, 258, 210, 221]
- long_name :
- h point nominal latitude
- units :
- degrees_north
array(0.06249997)
- yq()float64-0.0625
- axis :
- Y
- domain_decomposition :
- [209, 257, 209, 221]
- long_name :
- q point nominal latitude
- units :
- degrees_north
array(-0.06249997)
- zi(zi)float64-523.8 -481.0 -442.5 ... -2.5 -0.0
- axis :
- Z
- long_name :
- Interface pseudo-depth, -z*
- positive :
- up
- units :
- meter
array([-523.8 , -481.01, -442.51, -407.64, -375.88, -346.78, -319.99, -295.22, -272.22, -250.8 , -230.78, -212.02, -194.41, -177.85, -162.26, -147.57, -133.72, -120.66, -108.37, -96.83, -86.02, -75.94, -66.57, -57.91, -49.94, -42.66, -36.05, -30.1 , -24.81, -20.16, -16.15, -12.77, -10. , -7.5 , -5. , -2.5 , -0. ]) - zl(zl)float64-547.8 -502.4 ... -3.75 -1.25
- axis :
- Z
- long_name :
- Layer pseudo-depth, -z*
- positive :
- up
- units :
- meter
array([-547.75 , -502.405, -461.76 , -425.075, -391.76 , -361.33 , -333.385, -307.605, -283.72 , -261.51 , -240.79 , -221.4 , -203.215, -186.13 , -170.055, -154.915, -140.645, -127.19 , -114.515, -102.6 , -91.425, -80.98 , -71.255, -62.24 , -53.925, -46.3 , -39.355, -33.075, -27.455, -22.485, -18.155, -14.46 , -11.385, -8.75 , -6.25 , -3.75 , -1.25 ]) - eucmax(time)float64dask.array<chunksize=(8760,), meta=np.ndarray>
- units :
- m
- long_name :
- EUC maximum
- positive :
- up
Array Chunk Bytes 1.00 MiB 68.44 kiB Shape (131400,) (8760,) Dask graph 15 chunks in 22 graph layers Data type float64 numpy.ndarray - mldT(time)float64dask.array<chunksize=(8760,), meta=np.ndarray>
- long_name :
- MLD$_θ$
- units :
- m
- description :
- Interpolate θi to 1m grid. Search for max depth where |dθ| > 0.15
Array Chunk Bytes 1.00 MiB 68.44 kiB Shape (131400,) (8760,) Dask graph 15 chunks in 24 graph layers Data type float64 numpy.ndarray - dcl_mask(zi, time)booldask.array<chunksize=(37, 8760), meta=np.ndarray>
- description :
- True when 5m below mldT and above eucmax.
Array Chunk Bytes 4.64 MiB 316.52 kiB Shape (37, 131400) (37, 8760) Dask graph 15 chunks in 57 graph layers Data type bool numpy.ndarray - oni(time)float320.92 0.92 0.92 ... -0.87 -0.87
array([ 0.92, 0.92, 0.92, ..., -0.87, -0.87, -0.87], dtype=float32)
- en_mask(time)boolTrue True True ... False False
array([ True, True, True, ..., False, False, False])
- ln_mask(time)boolFalse False False ... True True
array([False, False, False, ..., True, True, True])
- warm_mask(time)boolFalse False False ... True True
array([False, False, False, ..., True, True, True])
- cool_mask(time)boolTrue True True ... False False
array([ True, True, True, ..., False, False, False])
- enso_transition(time)<U12'El-Nino cool' ... 'La-Nina warm'
- description :
- Warner & Moum (2019) ENSO transition phase; El-Nino = ONI > 0.5 for at least 6 months; La-Nina = ONI < -0.5 for at least 6 months
array(['El-Nino cool', 'El-Nino cool', 'El-Nino cool', ..., 'La-Nina warm', 'La-Nina warm', 'La-Nina warm'], dtype='<U12')
- KPP_BulkRi(time, zl)float32dask.array<chunksize=(8760, 37), meta=np.ndarray>
- cell_measures :
- volume: volcello area: areacello
- cell_methods :
- area:mean zl:mean yh:mean xh:mean time: mean
- long_name :
- Bulk Richardson number used to find the OBL depth used by [CVMix] KPP
- time_avg_info :
- average_T1,average_T2,average_DT
- units :
- nondim
Array Chunk Bytes 18.55 MiB 1.24 MiB Shape (131400, 37) (8760, 37) Dask graph 15 chunks in 6 graph layers Data type float32 numpy.ndarray - KPP_NLtransport_heat(time, zi)float32dask.array<chunksize=(8760, 37), meta=np.ndarray>
- cell_measures :
- area: areacello
- cell_methods :
- area:mean zi:point yh:mean xh:mean time: mean
- long_name :
- Non-local transport (Cs*G(sigma)) for heat, as calculated by [CVMix] KPP
- time_avg_info :
- average_T1,average_T2,average_DT
- units :
- nondim
Array Chunk Bytes 18.55 MiB 1.24 MiB Shape (131400, 37) (8760, 37) Dask graph 15 chunks in 6 graph layers Data type float32 numpy.ndarray - KPP_OBLdepth(time)float32dask.array<chunksize=(131400,), meta=np.ndarray>
- cell_measures :
- area: areacello
- cell_methods :
- area:mean yh:mean xh:mean time: mean
- long_name :
- Thickness of the surface Ocean Boundary Layer calculated by [CVMix] KPP
- time_avg_info :
- average_T1,average_T2,average_DT
- units :
- meter
Array Chunk Bytes 513.28 kiB 513.28 kiB Shape (131400,) (131400,) Dask graph 1 chunks in 4 graph layers Data type float32 numpy.ndarray - KPP_buoyFlux(time, zi)float32dask.array<chunksize=(8760, 37), meta=np.ndarray>
- cell_measures :
- area: areacello
- cell_methods :
- area:mean zi:point yh:mean xh:mean time: mean
- long_name :
- Surface (and penetrating) buoyancy flux, as used by [CVMix] KPP
- time_avg_info :
- average_T1,average_T2,average_DT
- units :
- m2/s3
Array Chunk Bytes 18.55 MiB 1.24 MiB Shape (131400, 37) (8760, 37) Dask graph 15 chunks in 6 graph layers Data type float32 numpy.ndarray - KPP_ustar(time)float32dask.array<chunksize=(131400,), meta=np.ndarray>
- cell_measures :
- area: areacello
- cell_methods :
- area:mean yh:mean xh:mean time: mean
- long_name :
- Friction velocity, u*, as used by [CVMix] KPP
- time_avg_info :
- average_T1,average_T2,average_DT
- units :
- m/s
Array Chunk Bytes 513.28 kiB 513.28 kiB Shape (131400,) (131400,) Dask graph 1 chunks in 4 graph layers Data type float32 numpy.ndarray - KS_extra(time, zi)float32dask.array<chunksize=(8760, 37), meta=np.ndarray>
- cell_measures :
- area: areacello
- cell_methods :
- area:mean zi:point yh:mean xh:mean time: mean
- long_name :
- Double-diffusive diffusivity for salinity
- time_avg_info :
- average_T1,average_T2,average_DT
- units :
- m2 s-1
Array Chunk Bytes 18.55 MiB 1.24 MiB Shape (131400, 37) (8760, 37) Dask graph 15 chunks in 6 graph layers Data type float32 numpy.ndarray - KT_extra(time, zi)float32dask.array<chunksize=(8760, 37), meta=np.ndarray>
- cell_measures :
- area: areacello
- cell_methods :
- area:mean zi:point yh:mean xh:mean time: mean
- long_name :
- Double-diffusive diffusivity for temperature
- time_avg_info :
- average_T1,average_T2,average_DT
- units :
- m2 s-1
Array Chunk Bytes 18.55 MiB 1.24 MiB Shape (131400, 37) (8760, 37) Dask graph 15 chunks in 6 graph layers Data type float32 numpy.ndarray - Kd_heat(time, zi)float32dask.array<chunksize=(8760, 37), meta=np.ndarray>
- cell_measures :
- area: areacello
- cell_methods :
- area:mean zi:point yh:mean xh:mean time: mean
- long_name :
- Total diapycnal diffusivity for heat at interfaces
- standard_name :
- ocean_vertical_heat_diffusivity
- time_avg_info :
- average_T1,average_T2,average_DT
- units :
- m2 s-1
Array Chunk Bytes 18.55 MiB 1.24 MiB Shape (131400, 37) (8760, 37) Dask graph 15 chunks in 6 graph layers Data type float32 numpy.ndarray - Kd_salt(time, zi)float32dask.array<chunksize=(8760, 37), meta=np.ndarray>
- cell_measures :
- area: areacello
- cell_methods :
- area:mean zi:point yh:mean xh:mean time: mean
- long_name :
- Total diapycnal diffusivity for salt at interfaces
- time_avg_info :
- average_T1,average_T2,average_DT
- units :
- m2 s-1
Array Chunk Bytes 18.55 MiB 1.24 MiB Shape (131400, 37) (8760, 37) Dask graph 15 chunks in 6 graph layers Data type float32 numpy.ndarray - Kv_u(time, zl)float32dask.array<chunksize=(8760, 37), meta=np.ndarray>
- cell_methods :
- zl:mean yh:mean xq:point time: mean
- interp_method :
- none
- long_name :
- Total vertical viscosity at u-points
- standard_name :
- ocean_vertical_x_viscosity
- time_avg_info :
- average_T1,average_T2,average_DT
- units :
- m2 s-1
Array Chunk Bytes 18.55 MiB 1.24 MiB Shape (131400, 37) (8760, 37) Dask graph 15 chunks in 6 graph layers Data type float32 numpy.ndarray - Kv_v(time, zl)float32dask.array<chunksize=(8760, 37), meta=np.ndarray>
- cell_methods :
- zl:mean yq:point xh:mean time: mean
- interp_method :
- none
- long_name :
- Total vertical viscosity at v-points
- standard_name :
- ocean_vertical_y_viscosity
- time_avg_info :
- average_T1,average_T2,average_DT
- units :
- m2 s-1
Array Chunk Bytes 18.55 MiB 1.24 MiB Shape (131400, 37) (8760, 37) Dask graph 15 chunks in 6 graph layers Data type float32 numpy.ndarray - N2(time, zi)float32dask.array<chunksize=(8760, 37), meta=np.ndarray>
- cell_measures :
- area: areacello
- cell_methods :
- area:mean zi:point yh:mean xh:mean time: mean
- long_name :
- Buoyancy frequency squared
- time_avg_info :
- average_T1,average_T2,average_DT
- units :
- s-2
Array Chunk Bytes 18.55 MiB 1.24 MiB Shape (131400, 37) (8760, 37) Dask graph 15 chunks in 6 graph layers Data type float32 numpy.ndarray - SSH(time)float32dask.array<chunksize=(131400,), meta=np.ndarray>
- cell_measures :
- area: areacello
- cell_methods :
- area:mean yh:mean xh:mean time: mean
- long_name :
- Sea Surface Height
- time_avg_info :
- average_T1,average_T2,average_DT
- units :
- m
Array Chunk Bytes 513.28 kiB 513.28 kiB Shape (131400,) (131400,) Dask graph 1 chunks in 4 graph layers Data type float32 numpy.ndarray - SW(time)float32dask.array<chunksize=(131400,), meta=np.ndarray>
- cell_measures :
- area: areacello
- cell_methods :
- area:mean yh:mean xh:mean time: mean
- long_name :
- Shortwave radiation flux into ocean
- standard_name :
- net_downward_shortwave_flux_at_sea_water_surface
- time_avg_info :
- average_T1,average_T2,average_DT
- units :
- W m-2
Array Chunk Bytes 513.28 kiB 513.28 kiB Shape (131400,) (131400,) Dask graph 1 chunks in 4 graph layers Data type float32 numpy.ndarray - SW_pen(time)float32dask.array<chunksize=(131400,), meta=np.ndarray>
- cell_measures :
- area: areacello
- cell_methods :
- area:mean yh:mean xh:mean time: mean
- long_name :
- Penetrating shortwave radiation flux into ocean
- time_avg_info :
- average_T1,average_T2,average_DT
- units :
- W m-2
Array Chunk Bytes 513.28 kiB 513.28 kiB Shape (131400,) (131400,) Dask graph 1 chunks in 4 graph layers Data type float32 numpy.ndarray - Tflx_dia_diff(time, zi)float32dask.array<chunksize=(8760, 37), meta=np.ndarray>
- cell_measures :
- area: areacello
- cell_methods :
- area:mean zi:point yh:mean xh:mean time: mean
- long_name :
- Diffusive diapycnal temperature flux across interfaces
- standard_name :
- ocean_vertical_diffusive_heat_flux
- time_avg_info :
- average_T1,average_T2,average_DT
- units :
- degC m s-1
Array Chunk Bytes 18.55 MiB 1.24 MiB Shape (131400, 37) (8760, 37) Dask graph 15 chunks in 6 graph layers Data type float32 numpy.ndarray - dens(time, zl)float32dask.array<chunksize=(8760, 37), meta=np.ndarray>
- cell_measures :
- volume: volcello area: areacello
- cell_methods :
- area:mean zl:mean yh:mean xh:mean time: mean
- long_name :
- Sea Water Salinity
- standard_name :
- sea_water_potential_density
- time_avg_info :
- average_T1,average_T2,average_DT
- units :
- kg/m^3
Array Chunk Bytes 18.55 MiB 1.24 MiB Shape (131400, 37) (8760, 37) Dask graph 15 chunks in 6 graph layers Data type float32 numpy.ndarray - densT(time, zl)float32dask.array<chunksize=(8760, 37), meta=np.ndarray>
- cell_measures :
- volume: volcello area: areacello
- cell_methods :
- area:mean zl:mean yh:mean xh:mean time: mean
- long_name :
- Sea Water Potential Temperature
- standard_name :
- sea_water_potential_density
- time_avg_info :
- average_T1,average_T2,average_DT
- units :
- kg/m3
Array Chunk Bytes 18.55 MiB 1.24 MiB Shape (131400, 37) (8760, 37) Dask graph 15 chunks in 6 graph layers Data type float32 numpy.ndarray - h(time, zl)float32dask.array<chunksize=(8760, 37), meta=np.ndarray>
- cell_measures :
- volume: volcello area: areacello
- cell_methods :
- area:mean zl:sum yh:mean xh:mean time: mean
- long_name :
- Layer Thickness
- time_avg_info :
- average_T1,average_T2,average_DT
- units :
- m
Array Chunk Bytes 18.55 MiB 1.24 MiB Shape (131400, 37) (8760, 37) Dask graph 15 chunks in 6 graph layers Data type float32 numpy.ndarray - mlotst(time)float32dask.array<chunksize=(131400,), meta=np.ndarray>
- cell_measures :
- area: areacello
- cell_methods :
- area:mean yh:mean xh:mean time: mean
- long_name :
- Ocean Mixed Layer Thickness Defined by Sigma T
- standard_name :
- ocean_mixed_layer_thickness_defined_by_sigma_t
- time_avg_info :
- average_T1,average_T2,average_DT
- units :
- m
Array Chunk Bytes 513.28 kiB 513.28 kiB Shape (131400,) (131400,) Dask graph 1 chunks in 4 graph layers Data type float32 numpy.ndarray - net_heat_surface(time)float32dask.array<chunksize=(131400,), meta=np.ndarray>
- cell_measures :
- area: areacello
- cell_methods :
- area:mean yh:mean xh:mean time: mean
- long_name :
- Surface ocean heat flux from SW+LW+lat+sens+mass transfer+frazil+restore+seaice_melt_heat or flux adjustments
- standard_name :
- surface_downward_heat_flux_in_sea_water
- time_avg_info :
- average_T1,average_T2,average_DT
- units :
- W m-2
Array Chunk Bytes 513.28 kiB 513.28 kiB Shape (131400,) (131400,) Dask graph 1 chunks in 4 graph layers Data type float32 numpy.ndarray - ri_grad_shear(time, zi)float32dask.array<chunksize=(8760, 37), meta=np.ndarray>
- cell_measures :
- area: areacello
- cell_methods :
- area:mean zi:point yh:mean xh:mean time: mean
- long_name :
- Gradient Richarson number used by MOM_CVMix_shear module
- time_avg_info :
- average_T1,average_T2,average_DT
- units :
- nondim
Array Chunk Bytes 18.55 MiB 1.24 MiB Shape (131400, 37) (8760, 37) Dask graph 15 chunks in 6 graph layers Data type float32 numpy.ndarray - ri_grad_shear_orig(time, zi)float32dask.array<chunksize=(8760, 37), meta=np.ndarray>
- cell_measures :
- area: areacello
- cell_methods :
- area:mean zi:point yh:mean xh:mean time: mean
- long_name :
- Original gradient Richarson number, before smoothing was applied. This is part of the MOM_CVMix_shear module and only available
- time_avg_info :
- average_T1,average_T2,average_DT
- units :
- nondim
Array Chunk Bytes 18.55 MiB 1.24 MiB Shape (131400, 37) (8760, 37) Dask graph 15 chunks in 6 graph layers Data type float32 numpy.ndarray - so(time, zl)float32dask.array<chunksize=(8760, 37), meta=np.ndarray>
- cell_measures :
- volume: volcello area: areacello
- cell_methods :
- area:mean zl:mean yh:mean xh:mean time: mean
- long_name :
- Sea Water Salinity
- standard_name :
- sea_water_salinity
- time_avg_info :
- average_T1,average_T2,average_DT
- units :
- psu
Array Chunk Bytes 18.55 MiB 1.24 MiB Shape (131400, 37) (8760, 37) Dask graph 15 chunks in 6 graph layers Data type float32 numpy.ndarray - taux(time)float32dask.array<chunksize=(131400,), meta=np.ndarray>
- cell_methods :
- yh:mean xq:point time: mean
- interp_method :
- none
- long_name :
- Zonal surface stress from ocean interactions with atmos and ice
- standard_name :
- surface_downward_x_stress
- time_avg_info :
- average_T1,average_T2,average_DT
- units :
- Pa
Array Chunk Bytes 513.28 kiB 513.28 kiB Shape (131400,) (131400,) Dask graph 1 chunks in 4 graph layers Data type float32 numpy.ndarray - tauy(time)float32dask.array<chunksize=(131400,), meta=np.ndarray>
- cell_methods :
- yq:point xh:mean time: mean
- interp_method :
- none
- long_name :
- Meridional surface stress ocean interactions with atmos and ice
- standard_name :
- surface_downward_y_stress
- time_avg_info :
- average_T1,average_T2,average_DT
- units :
- Pa
Array Chunk Bytes 513.28 kiB 513.28 kiB Shape (131400,) (131400,) Dask graph 1 chunks in 4 graph layers Data type float32 numpy.ndarray - thetao(time, zl)float32dask.array<chunksize=(8760, 37), meta=np.ndarray>
- cell_measures :
- volume: volcello area: areacello
- cell_methods :
- area:mean zl:mean yh:mean xh:mean time: mean
- long_name :
- Sea Water Potential Temperature
- standard_name :
- sea_water_potential_temperature
- time_avg_info :
- average_T1,average_T2,average_DT
- units :
- degC
Array Chunk Bytes 18.55 MiB 1.24 MiB Shape (131400, 37) (8760, 37) Dask graph 15 chunks in 6 graph layers Data type float32 numpy.ndarray - uo(time, zl)float32dask.array<chunksize=(8760, 37), meta=np.ndarray>
- cell_methods :
- zl:mean yh:mean xq:point time: mean
- interp_method :
- none
- long_name :
- Sea Water X Velocity
- standard_name :
- sea_water_x_velocity
- time_avg_info :
- average_T1,average_T2,average_DT
- units :
- m s-1
Array Chunk Bytes 18.55 MiB 1.24 MiB Shape (131400, 37) (8760, 37) Dask graph 15 chunks in 6 graph layers Data type float32 numpy.ndarray - vo(time, zl)float32dask.array<chunksize=(8760, 37), meta=np.ndarray>
- cell_methods :
- zl:mean yq:point xh:mean time: mean
- interp_method :
- none
- long_name :
- Sea Water Y Velocity
- standard_name :
- sea_water_y_velocity
- time_avg_info :
- average_T1,average_T2,average_DT
- units :
- m s-1
Array Chunk Bytes 18.55 MiB 1.24 MiB Shape (131400, 37) (8760, 37) Dask graph 15 chunks in 6 graph layers Data type float32 numpy.ndarray - volcello(time, zl)float32dask.array<chunksize=(8760, 37), meta=np.ndarray>
- cell_methods :
- area:sum zl:sum yh:sum xh:sum time: mean
- long_name :
- Ocean grid-cell volume
- standard_name :
- ocean_volume
- time_avg_info :
- average_T1,average_T2,average_DT
- units :
- m3
Array Chunk Bytes 18.55 MiB 1.24 MiB Shape (131400, 37) (8760, 37) Dask graph 15 chunks in 6 graph layers Data type float32 numpy.ndarray - zos(time)float32dask.array<chunksize=(131400,), meta=np.ndarray>
- cell_measures :
- area: areacello
- cell_methods :
- area:mean yh:mean xh:mean time: mean
- long_name :
- Sea surface height above geoid
- standard_name :
- sea_surface_height_above_geoid
- time_avg_info :
- average_T1,average_T2,average_DT
- units :
- m
Array Chunk Bytes 513.28 kiB 513.28 kiB Shape (131400,) (131400,) Dask graph 1 chunks in 4 graph layers Data type float32 numpy.ndarray - α(time, zl)float32dask.array<chunksize=(8760, 37), meta=np.ndarray>
- standard_name :
- sea_water_thermal_expansion_coefficient
- units :
- C-1
Array Chunk Bytes 18.55 MiB 1.24 MiB Shape (131400, 37) (8760, 37) Dask graph 15 chunks in 6 graph layers Data type float32 numpy.ndarray - β(time, zl)float32dask.array<chunksize=(8760, 37), meta=np.ndarray>
- standard_name :
- sea_water_haline_contraction_coefficient
- units :
- kg/g
Array Chunk Bytes 18.55 MiB 1.24 MiB Shape (131400, 37) (8760, 37) Dask graph 15 chunks in 6 graph layers Data type float32 numpy.ndarray - Tz(time, zi)float32dask.array<chunksize=(8760, 36), meta=np.ndarray>
- long_name :
- $T_z$
- units :
- Cm$^{-1}$
Array Chunk Bytes 18.55 MiB 1.20 MiB Shape (131400, 37) (8760, 36) Dask graph 30 chunks in 24 graph layers Data type float32 numpy.ndarray - Sz(time, zi)float32dask.array<chunksize=(8760, 36), meta=np.ndarray>
- long_name :
- $S_z$
- units :
- m$^{-1}$
Array Chunk Bytes 18.55 MiB 1.20 MiB Shape (131400, 37) (8760, 36) Dask graph 30 chunks in 24 graph layers Data type float32 numpy.ndarray - N2T(time, zi)float32dask.array<chunksize=(8760, 36), meta=np.ndarray>
- long_name :
- $N_T^2$
- units :
- s$^{-2}$
Array Chunk Bytes 18.55 MiB 1.20 MiB Shape (131400, 37) (8760, 36) Dask graph 30 chunks in 28 graph layers Data type float32 numpy.ndarray - S2(time, zi)float32dask.array<chunksize=(8760, 36), meta=np.ndarray>
- long_name :
- $S^2$
- units :
- s$^{-2}$
Array Chunk Bytes 18.55 MiB 1.20 MiB Shape (131400, 37) (8760, 36) Dask graph 30 chunks in 51 graph layers Data type float32 numpy.ndarray - shred2(time, zi)float32dask.array<chunksize=(8760, 36), meta=np.ndarray>
- long_name :
- $Sh_{red}^2$
- units :
- $s^{-2}$
Array Chunk Bytes 18.55 MiB 1.20 MiB Shape (131400, 37) (8760, 36) Dask graph 30 chunks in 67 graph layers Data type float32 numpy.ndarray - Rig_T(time, zi)float32dask.array<chunksize=(8760, 36), meta=np.ndarray>
- long_name :
- $Ri^g_T$
Array Chunk Bytes 18.55 MiB 1.20 MiB Shape (131400, 37) (8760, 36) Dask graph 30 chunks in 66 graph layers Data type float32 numpy.ndarray - Rig(time, zi)float32dask.array<chunksize=(8760, 36), meta=np.ndarray>
- cell_measures :
- area: areacello
- cell_methods :
- area:mean zi:point yh:mean xh:mean time: mean
- long_name :
- $Ri^g$
- time_avg_info :
- average_T1,average_T2,average_DT
Array Chunk Bytes 18.55 MiB 1.20 MiB Shape (131400, 37) (8760, 36) Dask graph 30 chunks in 56 graph layers Data type float32 numpy.ndarray - tau(time)float32dask.array<chunksize=(131400,), meta=np.ndarray>
- cell_methods :
- yh:mean xq:point time: mean
- interp_method :
- none
- long_name :
- Zonal surface stress from ocean interactions with atmos and ice
- time_avg_info :
- average_T1,average_T2,average_DT
- units :
- Pa
Array Chunk Bytes 513.28 kiB 513.28 kiB Shape (131400,) (131400,) Dask graph 1 chunks in 10 graph layers Data type float32 numpy.ndarray - Jb(time, zi)float32dask.array<chunksize=(8760, 36), meta=np.ndarray>
- cell_measures :
- area: areacello
- cell_methods :
- area:mean zi:point yh:mean xh:mean time: mean
- long_name :
- Total diapycnal diffusivity for heat at interfaces
- standard_name :
- ocean_vertical_diffusive_buoyancy_flux
- time_avg_info :
- average_T1,average_T2,average_DT
- units :
- m2 s-1
Array Chunk Bytes 18.55 MiB 1.20 MiB Shape (131400, 37) (8760, 36) Dask graph 30 chunks in 63 graph layers Data type float32 numpy.ndarray - Jq(time, zi)float64dask.array<chunksize=(8760, 37), meta=np.ndarray>
- units :
- W/m^2
- long_name :
- $J_q^t$
Array Chunk Bytes 37.09 MiB 2.47 MiB Shape (131400, 37) (8760, 37) Dask graph 15 chunks in 7 graph layers Data type float64 numpy.ndarray - ν(time, zl)float32dask.array<chunksize=(8760, 37), meta=np.ndarray>
- cell_methods :
- zl:mean yh:mean xq:point time: mean
- interp_method :
- none
- long_name :
- Total vertical viscosity at u-points
- standard_name :
- ocean_vertical_momentum_diffusivity
- time_avg_info :
- average_T1,average_T2,average_DT
- units :
- m2 s-1
Array Chunk Bytes 18.55 MiB 1.24 MiB Shape (131400, 37) (8760, 37) Dask graph 15 chunks in 6 graph layers Data type float32 numpy.ndarray - shear_prod(time, zi)float32dask.array<chunksize=(8760, 36), meta=np.ndarray>
- long_name :
- $SP$
- units :
- W/kg
Array Chunk Bytes 18.55 MiB 1.20 MiB Shape (131400, 37) (8760, 36) Dask graph 30 chunks in 72 graph layers Data type float32 numpy.ndarray - eps(time, zi)float32dask.array<chunksize=(8760, 36), meta=np.ndarray>
- long_name :
- $ε$
- units :
- W/kg
Array Chunk Bytes 18.55 MiB 1.20 MiB Shape (131400, 37) (8760, 36) Dask graph 30 chunks in 122 graph layers Data type float32 numpy.ndarray - chi(time, zi)float32dask.array<chunksize=(8760, 36), meta=np.ndarray>
- long_name :
- $χ$
- units :
- C^2/s
Array Chunk Bytes 18.55 MiB 1.20 MiB Shape (131400, 37) (8760, 36) Dask graph 30 chunks in 31 graph layers Data type float32 numpy.ndarray - Rif(time, zi)float32dask.array<chunksize=(8760, 36), meta=np.ndarray>
- cell_measures :
- area: areacello
- cell_methods :
- area:mean zi:point yh:mean xh:mean time: mean
- long_name :
- Total diapycnal diffusivity for heat at interfaces
- standard_name :
- flux_richardson_number
- time_avg_info :
- average_T1,average_T2,average_DT
- units :
- m2 s-1
Array Chunk Bytes 18.55 MiB 1.20 MiB Shape (131400, 37) (8760, 36) Dask graph 30 chunks in 123 graph layers Data type float32 numpy.ndarray - sst(time)float32dask.array<chunksize=(8760,), meta=np.ndarray>
- cell_measures :
- volume: volcello area: areacello
- cell_methods :
- area:mean zl:mean yh:mean xh:mean time: mean
- long_name :
- $SST$
- standard_name :
- sea_surface_temperature
- time_avg_info :
- average_T1,average_T2,average_DT
- units :
- degC
Array Chunk Bytes 513.28 kiB 34.22 kiB Shape (131400,) (8760,) Dask graph 15 chunks in 6 graph layers Data type float32 numpy.ndarray
- title :
- KD=0, KV=0
<xarray.DatasetView> Dimensions: (time: 131400, zl: 37, zi: 37, nv: 2) Coordinates: (12/16) * nv (nv) float64 1.0 2.0 * time (time) datetime64[ns] 2003-01-01T00:30:00 ... 2017-... xh float64 -140.0 yh float64 0.0625 yq float64 -0.0625 * zi (zi) float64 -523.8 -481.0 -442.5 ... -5.0 -2.5 -0.0 ... ... oni (time) float32 0.92 0.92 0.92 ... -0.87 -0.87 -0.87 en_mask (time) bool True True True True ... False False False ln_mask (time) bool False False False False ... True True True warm_mask (time) bool False False False False ... True True True cool_mask (time) bool True True True True ... False False False enso_transition (time) <U12 'El-Nino cool' ... 'La-Nina warm' Data variables: (12/49) KPP_BulkRi (time, zl) float32 dask.array<chunksize=(8760, 37), meta=np.ndarray> KPP_NLtransport_heat (time, zi) float32 dask.array<chunksize=(8760, 37), meta=np.ndarray> KPP_OBLdepth (time) float32 dask.array<chunksize=(131400,), meta=np.ndarray> KPP_buoyFlux (time, zi) float32 dask.array<chunksize=(8760, 37), meta=np.ndarray> KPP_ustar (time) float32 dask.array<chunksize=(131400,), meta=np.ndarray> KS_extra (time, zi) float32 dask.array<chunksize=(8760, 37), meta=np.ndarray> ... ... ν (time, zl) float32 dask.array<chunksize=(8760, 37), meta=np.ndarray> shear_prod (time, zi) float32 dask.array<chunksize=(8760, 36), meta=np.ndarray> eps (time, zi) float32 dask.array<chunksize=(8760, 36), meta=np.ndarray> chi (time, zi) float32 dask.array<chunksize=(8760, 36), meta=np.ndarray> Rif (time, zi) float32 dask.array<chunksize=(8760, 36), meta=np.ndarray> sst (time) float32 dask.array<chunksize=(8760,), meta=np.ndarray> Attributes: title: KD=0, KV=0new_baseline.hb- time: 131400
- zl: 37
- zi: 37
- nv: 2
- nv(nv)float641.0 2.0
- long_name :
- vertex number
array([1., 2.])
- time(time)datetime64[ns]2003-01-01T00:30:00 ... 2017-12-...
array(['2003-01-01T00:30:00.000000000', '2003-01-01T01:30:00.000000000', '2003-01-01T02:30:00.000000000', ..., '2017-12-31T21:30:00.000000000', '2017-12-31T22:30:00.000000000', '2017-12-31T23:30:00.000000000'], dtype='datetime64[ns]') - xh()float64-140.0
- axis :
- X
- domain_decomposition :
- [220, 222, 220, 221]
- long_name :
- h point nominal longitude
- units :
- degrees_east
array(-140.)
- yh()float640.0625
- axis :
- Y
- domain_decomposition :
- [210, 258, 210, 221]
- long_name :
- h point nominal latitude
- units :
- degrees_north
array(0.06249997)
- yq()float64-0.0625
- axis :
- Y
- domain_decomposition :
- [209, 257, 209, 221]
- long_name :
- q point nominal latitude
- units :
- degrees_north
array(-0.06249997)
- zi(zi)float64-523.8 -481.0 -442.5 ... -2.5 -0.0
- axis :
- Z
- long_name :
- Interface pseudo-depth, -z*
- positive :
- up
- units :
- meter
array([-523.8 , -481.01, -442.51, -407.64, -375.88, -346.78, -319.99, -295.22, -272.22, -250.8 , -230.78, -212.02, -194.41, -177.85, -162.26, -147.57, -133.72, -120.66, -108.37, -96.83, -86.02, -75.94, -66.57, -57.91, -49.94, -42.66, -36.05, -30.1 , -24.81, -20.16, -16.15, -12.77, -10. , -7.5 , -5. , -2.5 , -0. ]) - zl(zl)float64-547.8 -502.4 ... -3.75 -1.25
- axis :
- Z
- long_name :
- Layer pseudo-depth, -z*
- positive :
- up
- units :
- meter
array([-547.75 , -502.405, -461.76 , -425.075, -391.76 , -361.33 , -333.385, -307.605, -283.72 , -261.51 , -240.79 , -221.4 , -203.215, -186.13 , -170.055, -154.915, -140.645, -127.19 , -114.515, -102.6 , -91.425, -80.98 , -71.255, -62.24 , -53.925, -46.3 , -39.355, -33.075, -27.455, -22.485, -18.155, -14.46 , -11.385, -8.75 , -6.25 , -3.75 , -1.25 ]) - eucmax(time)float64dask.array<chunksize=(8760,), meta=np.ndarray>
- units :
- m
- long_name :
- EUC maximum
- positive :
- up
Array Chunk Bytes 1.00 MiB 68.44 kiB Shape (131400,) (8760,) Dask graph 15 chunks in 21 graph layers Data type float64 numpy.ndarray - mldT(time)float64dask.array<chunksize=(8760,), meta=np.ndarray>
- long_name :
- MLD$_θ$
- units :
- m
- description :
- Interpolate θi to 1m grid. Search for max depth where |dθ| > 0.15
Array Chunk Bytes 1.00 MiB 68.44 kiB Shape (131400,) (8760,) Dask graph 15 chunks in 23 graph layers Data type float64 numpy.ndarray - dcl_mask(zi, time)booldask.array<chunksize=(37, 8760), meta=np.ndarray>
- description :
- True when 5m below mldT and above eucmax.
Array Chunk Bytes 4.64 MiB 316.52 kiB Shape (37, 131400) (37, 8760) Dask graph 15 chunks in 56 graph layers Data type bool numpy.ndarray - oni(time)float320.63 0.63 0.63 ... -0.87 -0.87
array([ 0.63, 0.63, 0.63, ..., -0.87, -0.87, -0.87], dtype=float32)
- en_mask(time)boolFalse False False ... False False
array([False, False, False, ..., False, False, False])
- ln_mask(time)boolFalse False False ... True True
array([False, False, False, ..., True, True, True])
- warm_mask(time)boolTrue True True ... True True True
array([ True, True, True, ..., True, True, True])
- cool_mask(time)boolFalse False False ... False False
array([False, False, False, ..., False, False, False])
- enso_transition(time)<U12'____________' ... 'La-Nina warm'
- description :
- Warner & Moum (2019) ENSO transition phase; El-Nino = ONI > 0.5 for at least 6 months; La-Nina = ONI < -0.5 for at least 6 months
array(['____________', '____________', '____________', ..., 'La-Nina warm', 'La-Nina warm', 'La-Nina warm'], dtype='<U12')
- KPP_BulkRi(time, zl)float32dask.array<chunksize=(8760, 37), meta=np.ndarray>
- cell_measures :
- volume: volcello area: areacello
- cell_methods :
- area:mean zl:mean yh:mean xh:mean time: mean
- long_name :
- Bulk Richardson number used to find the OBL depth used by [CVMix] KPP
- time_avg_info :
- average_T1,average_T2,average_DT
- units :
- nondim
Array Chunk Bytes 18.55 MiB 1.24 MiB Shape (131400, 37) (8760, 37) Dask graph 15 chunks in 5 graph layers Data type float32 numpy.ndarray - KPP_NLtransport_heat(time, zi)float32dask.array<chunksize=(8760, 37), meta=np.ndarray>
- cell_measures :
- area: areacello
- cell_methods :
- area:mean zi:point yh:mean xh:mean time: mean
- long_name :
- Non-local transport (Cs*G(sigma)) for heat, as calculated by [CVMix] KPP
- time_avg_info :
- average_T1,average_T2,average_DT
- units :
- nondim
Array Chunk Bytes 18.55 MiB 1.24 MiB Shape (131400, 37) (8760, 37) Dask graph 15 chunks in 5 graph layers Data type float32 numpy.ndarray - KPP_OBLdepth(time)float32dask.array<chunksize=(131400,), meta=np.ndarray>
- cell_measures :
- area: areacello
- cell_methods :
- area:mean yh:mean xh:mean time: mean
- long_name :
- Thickness of the surface Ocean Boundary Layer calculated by [CVMix] KPP
- time_avg_info :
- average_T1,average_T2,average_DT
- units :
- meter
Array Chunk Bytes 513.28 kiB 513.28 kiB Shape (131400,) (131400,) Dask graph 1 chunks in 3 graph layers Data type float32 numpy.ndarray - KPP_buoyFlux(time, zi)float32dask.array<chunksize=(8760, 37), meta=np.ndarray>
- cell_measures :
- area: areacello
- cell_methods :
- area:mean zi:point yh:mean xh:mean time: mean
- long_name :
- Surface (and penetrating) buoyancy flux, as used by [CVMix] KPP
- time_avg_info :
- average_T1,average_T2,average_DT
- units :
- m2/s3
Array Chunk Bytes 18.55 MiB 1.24 MiB Shape (131400, 37) (8760, 37) Dask graph 15 chunks in 5 graph layers Data type float32 numpy.ndarray - KPP_ustar(time)float32dask.array<chunksize=(131400,), meta=np.ndarray>
- cell_measures :
- area: areacello
- cell_methods :
- area:mean yh:mean xh:mean time: mean
- long_name :
- Friction velocity, u*, as used by [CVMix] KPP
- time_avg_info :
- average_T1,average_T2,average_DT
- units :
- m/s
Array Chunk Bytes 513.28 kiB 513.28 kiB Shape (131400,) (131400,) Dask graph 1 chunks in 3 graph layers Data type float32 numpy.ndarray - KS_extra(time, zi)float32dask.array<chunksize=(8760, 37), meta=np.ndarray>
- cell_measures :
- area: areacello
- cell_methods :
- area:mean zi:point yh:mean xh:mean time: mean
- long_name :
- Double-diffusive diffusivity for salinity
- time_avg_info :
- average_T1,average_T2,average_DT
- units :
- m2 s-1
Array Chunk Bytes 18.55 MiB 1.24 MiB Shape (131400, 37) (8760, 37) Dask graph 15 chunks in 5 graph layers Data type float32 numpy.ndarray - KT_extra(time, zi)float32dask.array<chunksize=(8760, 37), meta=np.ndarray>
- cell_measures :
- area: areacello
- cell_methods :
- area:mean zi:point yh:mean xh:mean time: mean
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- Double-diffusive diffusivity for temperature
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- units :
- m2 s-1
Array Chunk Bytes 18.55 MiB 1.24 MiB Shape (131400, 37) (8760, 37) Dask graph 15 chunks in 5 graph layers Data type float32 numpy.ndarray - Kd_heat(time, zi)float32dask.array<chunksize=(8760, 37), meta=np.ndarray>
- cell_measures :
- area: areacello
- cell_methods :
- area:mean zi:point yh:mean xh:mean time: mean
- long_name :
- Total diapycnal diffusivity for heat at interfaces
- standard_name :
- ocean_vertical_heat_diffusivity
- time_avg_info :
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- units :
- m2 s-1
Array Chunk Bytes 18.55 MiB 1.24 MiB Shape (131400, 37) (8760, 37) Dask graph 15 chunks in 5 graph layers Data type float32 numpy.ndarray - Kd_salt(time, zi)float32dask.array<chunksize=(8760, 37), meta=np.ndarray>
- cell_measures :
- area: areacello
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- area:mean zi:point yh:mean xh:mean time: mean
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- units :
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Array Chunk Bytes 18.55 MiB 1.24 MiB Shape (131400, 37) (8760, 37) Dask graph 15 chunks in 5 graph layers Data type float32 numpy.ndarray - Kv_u(time, zl)float32dask.array<chunksize=(8760, 37), meta=np.ndarray>
- cell_methods :
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- interp_method :
- none
- long_name :
- Total vertical viscosity at u-points
- standard_name :
- ocean_vertical_x_viscosity
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- units :
- m2 s-1
Array Chunk Bytes 18.55 MiB 1.24 MiB Shape (131400, 37) (8760, 37) Dask graph 15 chunks in 5 graph layers Data type float32 numpy.ndarray - Kv_v(time, zl)float32dask.array<chunksize=(8760, 37), meta=np.ndarray>
- cell_methods :
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- interp_method :
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- units :
- m2 s-1
Array Chunk Bytes 18.55 MiB 1.24 MiB Shape (131400, 37) (8760, 37) Dask graph 15 chunks in 5 graph layers Data type float32 numpy.ndarray - N2(time, zi)float32dask.array<chunksize=(8760, 37), meta=np.ndarray>
- cell_measures :
- area: areacello
- cell_methods :
- area:mean zi:point yh:mean xh:mean time: mean
- long_name :
- Buoyancy frequency squared
- time_avg_info :
- average_T1,average_T2,average_DT
- units :
- s-2
Array Chunk Bytes 18.55 MiB 1.24 MiB Shape (131400, 37) (8760, 37) Dask graph 15 chunks in 5 graph layers Data type float32 numpy.ndarray - SSH(time)float32dask.array<chunksize=(131400,), meta=np.ndarray>
- cell_measures :
- area: areacello
- cell_methods :
- area:mean yh:mean xh:mean time: mean
- long_name :
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- units :
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Array Chunk Bytes 513.28 kiB 513.28 kiB Shape (131400,) (131400,) Dask graph 1 chunks in 3 graph layers Data type float32 numpy.ndarray - SW(time)float32dask.array<chunksize=(131400,), meta=np.ndarray>
- cell_measures :
- area: areacello
- cell_methods :
- area:mean yh:mean xh:mean time: mean
- long_name :
- Shortwave radiation flux into ocean
- standard_name :
- net_downward_shortwave_flux_at_sea_water_surface
- time_avg_info :
- average_T1,average_T2,average_DT
- units :
- W m-2
Array Chunk Bytes 513.28 kiB 513.28 kiB Shape (131400,) (131400,) Dask graph 1 chunks in 3 graph layers Data type float32 numpy.ndarray - SW_pen(time)float32dask.array<chunksize=(131400,), meta=np.ndarray>
- cell_measures :
- area: areacello
- cell_methods :
- area:mean yh:mean xh:mean time: mean
- long_name :
- Penetrating shortwave radiation flux into ocean
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- average_T1,average_T2,average_DT
- units :
- W m-2
Array Chunk Bytes 513.28 kiB 513.28 kiB Shape (131400,) (131400,) Dask graph 1 chunks in 3 graph layers Data type float32 numpy.ndarray - Tflx_dia_diff(time, zi)float32dask.array<chunksize=(8760, 37), meta=np.ndarray>
- cell_measures :
- area: areacello
- cell_methods :
- area:mean zi:point yh:mean xh:mean time: mean
- long_name :
- Diffusive diapycnal temperature flux across interfaces
- standard_name :
- ocean_vertical_diffusive_heat_flux
- time_avg_info :
- average_T1,average_T2,average_DT
- units :
- degC m s-1
Array Chunk Bytes 18.55 MiB 1.24 MiB Shape (131400, 37) (8760, 37) Dask graph 15 chunks in 5 graph layers Data type float32 numpy.ndarray - dens(time, zl)float32dask.array<chunksize=(8760, 37), meta=np.ndarray>
- cell_measures :
- volume: volcello area: areacello
- cell_methods :
- area:mean zl:mean yh:mean xh:mean time: mean
- long_name :
- Sea Water Salinity
- standard_name :
- sea_water_potential_density
- time_avg_info :
- average_T1,average_T2,average_DT
- units :
- kg/m^3
Array Chunk Bytes 18.55 MiB 1.24 MiB Shape (131400, 37) (8760, 37) Dask graph 15 chunks in 5 graph layers Data type float32 numpy.ndarray - densT(time, zl)float32dask.array<chunksize=(8760, 37), meta=np.ndarray>
- cell_measures :
- volume: volcello area: areacello
- cell_methods :
- area:mean zl:mean yh:mean xh:mean time: mean
- long_name :
- Sea Water Potential Temperature
- standard_name :
- sea_water_potential_density
- time_avg_info :
- average_T1,average_T2,average_DT
- units :
- kg/m3
Array Chunk Bytes 18.55 MiB 1.24 MiB Shape (131400, 37) (8760, 37) Dask graph 15 chunks in 5 graph layers Data type float32 numpy.ndarray - h(time, zl)float32dask.array<chunksize=(8760, 37), meta=np.ndarray>
- cell_measures :
- volume: volcello area: areacello
- cell_methods :
- area:mean zl:sum yh:mean xh:mean time: mean
- long_name :
- Layer Thickness
- time_avg_info :
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- units :
- m
Array Chunk Bytes 18.55 MiB 1.24 MiB Shape (131400, 37) (8760, 37) Dask graph 15 chunks in 5 graph layers Data type float32 numpy.ndarray - mlotst(time)float32dask.array<chunksize=(131400,), meta=np.ndarray>
- cell_measures :
- area: areacello
- cell_methods :
- area:mean yh:mean xh:mean time: mean
- long_name :
- Ocean Mixed Layer Thickness Defined by Sigma T
- standard_name :
- ocean_mixed_layer_thickness_defined_by_sigma_t
- time_avg_info :
- average_T1,average_T2,average_DT
- units :
- m
Array Chunk Bytes 513.28 kiB 513.28 kiB Shape (131400,) (131400,) Dask graph 1 chunks in 3 graph layers Data type float32 numpy.ndarray - net_heat_surface(time)float32dask.array<chunksize=(131400,), meta=np.ndarray>
- cell_measures :
- area: areacello
- cell_methods :
- area:mean yh:mean xh:mean time: mean
- long_name :
- Surface ocean heat flux from SW+LW+lat+sens+mass transfer+frazil+restore+seaice_melt_heat or flux adjustments
- standard_name :
- surface_downward_heat_flux_in_sea_water
- time_avg_info :
- average_T1,average_T2,average_DT
- units :
- W m-2
Array Chunk Bytes 513.28 kiB 513.28 kiB Shape (131400,) (131400,) Dask graph 1 chunks in 3 graph layers Data type float32 numpy.ndarray - ri_grad_shear(time, zi)float32dask.array<chunksize=(8760, 37), meta=np.ndarray>
- cell_measures :
- area: areacello
- cell_methods :
- area:mean zi:point yh:mean xh:mean time: mean
- long_name :
- Gradient Richarson number used by MOM_CVMix_shear module
- time_avg_info :
- average_T1,average_T2,average_DT
- units :
- nondim
Array Chunk Bytes 18.55 MiB 1.24 MiB Shape (131400, 37) (8760, 37) Dask graph 15 chunks in 5 graph layers Data type float32 numpy.ndarray - ri_grad_shear_orig(time, zi)float32dask.array<chunksize=(8760, 37), meta=np.ndarray>
- cell_measures :
- area: areacello
- cell_methods :
- area:mean zi:point yh:mean xh:mean time: mean
- long_name :
- Original gradient Richarson number, before smoothing was applied. This is part of the MOM_CVMix_shear module and only available
- time_avg_info :
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- units :
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Array Chunk Bytes 18.55 MiB 1.24 MiB Shape (131400, 37) (8760, 37) Dask graph 15 chunks in 5 graph layers Data type float32 numpy.ndarray - so(time, zl)float32dask.array<chunksize=(8760, 37), meta=np.ndarray>
- cell_measures :
- volume: volcello area: areacello
- cell_methods :
- area:mean zl:mean yh:mean xh:mean time: mean
- long_name :
- Sea Water Salinity
- standard_name :
- sea_water_salinity
- time_avg_info :
- average_T1,average_T2,average_DT
- units :
- psu
Array Chunk Bytes 18.55 MiB 1.24 MiB Shape (131400, 37) (8760, 37) Dask graph 15 chunks in 5 graph layers Data type float32 numpy.ndarray - taux(time)float32dask.array<chunksize=(131400,), meta=np.ndarray>
- cell_methods :
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- interp_method :
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- long_name :
- Zonal surface stress from ocean interactions with atmos and ice
- standard_name :
- surface_downward_x_stress
- time_avg_info :
- average_T1,average_T2,average_DT
- units :
- Pa
Array Chunk Bytes 513.28 kiB 513.28 kiB Shape (131400,) (131400,) Dask graph 1 chunks in 3 graph layers Data type float32 numpy.ndarray - tauy(time)float32dask.array<chunksize=(131400,), meta=np.ndarray>
- cell_methods :
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- interp_method :
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- long_name :
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- standard_name :
- surface_downward_y_stress
- time_avg_info :
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- units :
- Pa
Array Chunk Bytes 513.28 kiB 513.28 kiB Shape (131400,) (131400,) Dask graph 1 chunks in 3 graph layers Data type float32 numpy.ndarray - thetao(time, zl)float32dask.array<chunksize=(8760, 37), meta=np.ndarray>
- cell_measures :
- volume: volcello area: areacello
- cell_methods :
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- long_name :
- Sea Water Potential Temperature
- standard_name :
- sea_water_potential_temperature
- time_avg_info :
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- units :
- degC
Array Chunk Bytes 18.55 MiB 1.24 MiB Shape (131400, 37) (8760, 37) Dask graph 15 chunks in 5 graph layers Data type float32 numpy.ndarray - uo(time, zl)float32dask.array<chunksize=(8760, 37), meta=np.ndarray>
- cell_methods :
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- interp_method :
- none
- long_name :
- Sea Water X Velocity
- standard_name :
- sea_water_x_velocity
- time_avg_info :
- average_T1,average_T2,average_DT
- units :
- m s-1
Array Chunk Bytes 18.55 MiB 1.24 MiB Shape (131400, 37) (8760, 37) Dask graph 15 chunks in 5 graph layers Data type float32 numpy.ndarray - vo(time, zl)float32dask.array<chunksize=(8760, 37), meta=np.ndarray>
- cell_methods :
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- interp_method :
- none
- long_name :
- Sea Water Y Velocity
- standard_name :
- sea_water_y_velocity
- time_avg_info :
- average_T1,average_T2,average_DT
- units :
- m s-1
Array Chunk Bytes 18.55 MiB 1.24 MiB Shape (131400, 37) (8760, 37) Dask graph 15 chunks in 5 graph layers Data type float32 numpy.ndarray - volcello(time, zl)float32dask.array<chunksize=(8760, 37), meta=np.ndarray>
- cell_methods :
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- long_name :
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- standard_name :
- ocean_volume
- time_avg_info :
- average_T1,average_T2,average_DT
- units :
- m3
Array Chunk Bytes 18.55 MiB 1.24 MiB Shape (131400, 37) (8760, 37) Dask graph 15 chunks in 5 graph layers Data type float32 numpy.ndarray - zos(time)float32dask.array<chunksize=(131400,), meta=np.ndarray>
- cell_measures :
- area: areacello
- cell_methods :
- area:mean yh:mean xh:mean time: mean
- long_name :
- Sea surface height above geoid
- standard_name :
- sea_surface_height_above_geoid
- time_avg_info :
- average_T1,average_T2,average_DT
- units :
- m
Array Chunk Bytes 513.28 kiB 513.28 kiB Shape (131400,) (131400,) Dask graph 1 chunks in 3 graph layers Data type float32 numpy.ndarray - α(time, zl)float32dask.array<chunksize=(8760, 37), meta=np.ndarray>
- standard_name :
- sea_water_thermal_expansion_coefficient
- units :
- C-1
Array Chunk Bytes 18.55 MiB 1.24 MiB Shape (131400, 37) (8760, 37) Dask graph 15 chunks in 5 graph layers Data type float32 numpy.ndarray - β(time, zl)float32dask.array<chunksize=(8760, 37), meta=np.ndarray>
- standard_name :
- sea_water_haline_contraction_coefficient
- units :
- kg/g
Array Chunk Bytes 18.55 MiB 1.24 MiB Shape (131400, 37) (8760, 37) Dask graph 15 chunks in 5 graph layers Data type float32 numpy.ndarray - Tz(time, zi)float32dask.array<chunksize=(8760, 36), meta=np.ndarray>
- long_name :
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- units :
- Cm$^{-1}$
Array Chunk Bytes 18.55 MiB 1.20 MiB Shape (131400, 37) (8760, 36) Dask graph 30 chunks in 23 graph layers Data type float32 numpy.ndarray - Sz(time, zi)float32dask.array<chunksize=(8760, 36), meta=np.ndarray>
- long_name :
- $S_z$
- units :
- m$^{-1}$
Array Chunk Bytes 18.55 MiB 1.20 MiB Shape (131400, 37) (8760, 36) Dask graph 30 chunks in 23 graph layers Data type float32 numpy.ndarray - N2T(time, zi)float32dask.array<chunksize=(8760, 36), meta=np.ndarray>
- long_name :
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- units :
- s$^{-2}$
Array Chunk Bytes 18.55 MiB 1.20 MiB Shape (131400, 37) (8760, 36) Dask graph 30 chunks in 27 graph layers Data type float32 numpy.ndarray - S2(time, zi)float32dask.array<chunksize=(8760, 36), meta=np.ndarray>
- long_name :
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- units :
- s$^{-2}$
Array Chunk Bytes 18.55 MiB 1.20 MiB Shape (131400, 37) (8760, 36) Dask graph 30 chunks in 50 graph layers Data type float32 numpy.ndarray - shred2(time, zi)float32dask.array<chunksize=(8760, 36), meta=np.ndarray>
- long_name :
- $Sh_{red}^2$
- units :
- $s^{-2}$
Array Chunk Bytes 18.55 MiB 1.20 MiB Shape (131400, 37) (8760, 36) Dask graph 30 chunks in 66 graph layers Data type float32 numpy.ndarray - Rig_T(time, zi)float32dask.array<chunksize=(8760, 36), meta=np.ndarray>
- long_name :
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Array Chunk Bytes 18.55 MiB 1.20 MiB Shape (131400, 37) (8760, 36) Dask graph 30 chunks in 65 graph layers Data type float32 numpy.ndarray - Rig(time, zi)float32dask.array<chunksize=(8760, 36), meta=np.ndarray>
- cell_measures :
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- area:mean zi:point yh:mean xh:mean time: mean
- long_name :
- $Ri^g$
- time_avg_info :
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Array Chunk Bytes 18.55 MiB 1.20 MiB Shape (131400, 37) (8760, 36) Dask graph 30 chunks in 55 graph layers Data type float32 numpy.ndarray - tau(time)float32dask.array<chunksize=(131400,), meta=np.ndarray>
- cell_methods :
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- interp_method :
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- time_avg_info :
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- units :
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Array Chunk Bytes 513.28 kiB 513.28 kiB Shape (131400,) (131400,) Dask graph 1 chunks in 9 graph layers Data type float32 numpy.ndarray - Jb(time, zi)float32dask.array<chunksize=(8760, 36), meta=np.ndarray>
- cell_measures :
- area: areacello
- cell_methods :
- area:mean zi:point yh:mean xh:mean time: mean
- long_name :
- Total diapycnal diffusivity for heat at interfaces
- standard_name :
- ocean_vertical_diffusive_buoyancy_flux
- time_avg_info :
- average_T1,average_T2,average_DT
- units :
- m2 s-1
Array Chunk Bytes 18.55 MiB 1.20 MiB Shape (131400, 37) (8760, 36) Dask graph 30 chunks in 62 graph layers Data type float32 numpy.ndarray - Jq(time, zi)float64dask.array<chunksize=(8760, 37), meta=np.ndarray>
- units :
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- long_name :
- $J_q^t$
Array Chunk Bytes 37.09 MiB 2.47 MiB Shape (131400, 37) (8760, 37) Dask graph 15 chunks in 6 graph layers Data type float64 numpy.ndarray - ν(time, zl)float32dask.array<chunksize=(8760, 37), meta=np.ndarray>
- cell_methods :
- zl:mean yh:mean xq:point time: mean
- interp_method :
- none
- long_name :
- Total vertical viscosity at u-points
- standard_name :
- ocean_vertical_momentum_diffusivity
- time_avg_info :
- average_T1,average_T2,average_DT
- units :
- m2 s-1
Array Chunk Bytes 18.55 MiB 1.24 MiB Shape (131400, 37) (8760, 37) Dask graph 15 chunks in 5 graph layers Data type float32 numpy.ndarray - shear_prod(time, zi)float32dask.array<chunksize=(8760, 36), meta=np.ndarray>
- long_name :
- $SP$
- units :
- W/kg
Array Chunk Bytes 18.55 MiB 1.20 MiB Shape (131400, 37) (8760, 36) Dask graph 30 chunks in 71 graph layers Data type float32 numpy.ndarray - eps(time, zi)float32dask.array<chunksize=(8760, 36), meta=np.ndarray>
- long_name :
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- units :
- W/kg
Array Chunk Bytes 18.55 MiB 1.20 MiB Shape (131400, 37) (8760, 36) Dask graph 30 chunks in 121 graph layers Data type float32 numpy.ndarray - chi(time, zi)float32dask.array<chunksize=(8760, 36), meta=np.ndarray>
- long_name :
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- units :
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Array Chunk Bytes 18.55 MiB 1.20 MiB Shape (131400, 37) (8760, 36) Dask graph 30 chunks in 30 graph layers Data type float32 numpy.ndarray - Rif(time, zi)float32dask.array<chunksize=(8760, 36), meta=np.ndarray>
- cell_measures :
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- cell_methods :
- area:mean zi:point yh:mean xh:mean time: mean
- long_name :
- Total diapycnal diffusivity for heat at interfaces
- standard_name :
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- time_avg_info :
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- units :
- m2 s-1
Array Chunk Bytes 18.55 MiB 1.20 MiB Shape (131400, 37) (8760, 36) Dask graph 30 chunks in 122 graph layers Data type float32 numpy.ndarray - sst(time)float32dask.array<chunksize=(8760,), meta=np.ndarray>
- cell_measures :
- volume: volcello area: areacello
- cell_methods :
- area:mean zl:mean yh:mean xh:mean time: mean
- long_name :
- $SST$
- standard_name :
- sea_surface_temperature
- time_avg_info :
- average_T1,average_T2,average_DT
- units :
- degC
Array Chunk Bytes 513.28 kiB 34.22 kiB Shape (131400,) (8760,) Dask graph 15 chunks in 5 graph layers Data type float32 numpy.ndarray
- title :
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<xarray.DatasetView> Dimensions: (time: 131400, zl: 37, zi: 37, nv: 2) Coordinates: (12/16) * nv (nv) float64 1.0 2.0 * time (time) datetime64[ns] 2003-01-01T00:30:00 ... 2017-... xh float64 -140.0 yh float64 0.0625 yq float64 -0.0625 * zi (zi) float64 -523.8 -481.0 -442.5 ... -5.0 -2.5 -0.0 ... ... oni (time) float32 0.63 0.63 0.63 ... -0.87 -0.87 -0.87 en_mask (time) bool False False False ... False False False ln_mask (time) bool False False False False ... True True True warm_mask (time) bool True True True True ... True True True cool_mask (time) bool False False False ... False False False enso_transition (time) <U12 '____________' ... 'La-Nina warm' Data variables: (12/49) KPP_BulkRi (time, zl) float32 dask.array<chunksize=(8760, 37), meta=np.ndarray> KPP_NLtransport_heat (time, zi) float32 dask.array<chunksize=(8760, 37), meta=np.ndarray> KPP_OBLdepth (time) float32 dask.array<chunksize=(131400,), meta=np.ndarray> KPP_buoyFlux (time, zi) float32 dask.array<chunksize=(8760, 37), meta=np.ndarray> KPP_ustar (time) float32 dask.array<chunksize=(131400,), meta=np.ndarray> KS_extra (time, zi) float32 dask.array<chunksize=(8760, 37), meta=np.ndarray> ... ... ν (time, zl) float32 dask.array<chunksize=(8760, 37), meta=np.ndarray> shear_prod (time, zi) float32 dask.array<chunksize=(8760, 36), meta=np.ndarray> eps (time, zi) float32 dask.array<chunksize=(8760, 36), meta=np.ndarray> chi (time, zi) float32 dask.array<chunksize=(8760, 36), meta=np.ndarray> Rif (time, zi) float32 dask.array<chunksize=(8760, 36), meta=np.ndarray> sst (time) float32 dask.array<chunksize=(8760,), meta=np.ndarray> Attributes: title: KPP ν0=2.5, Ric=0.2, Ri0=0.5new_baseline.kpp.lmd.004- time: 131400
- zl: 37
- zi: 37
- nv: 2
- nv(nv)float641.0 2.0
- long_name :
- vertex number
array([1., 2.])
- time(time)datetime64[ns]2003-01-01T00:30:00 ... 2017-12-...
array(['2003-01-01T00:30:00.000000000', '2003-01-01T01:30:00.000000000', '2003-01-01T02:30:00.000000000', ..., '2017-12-31T21:30:00.000000000', '2017-12-31T22:30:00.000000000', '2017-12-31T23:30:00.000000000'], dtype='datetime64[ns]') - xh()float64-140.0
- axis :
- X
- domain_decomposition :
- [220, 222, 220, 221]
- long_name :
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- units :
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array(-140.)
- yh()float640.0625
- axis :
- Y
- domain_decomposition :
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- long_name :
- h point nominal latitude
- units :
- degrees_north
array(0.06249997)
- yq()float64-0.0625
- axis :
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- domain_decomposition :
- [209, 257, 209, 221]
- long_name :
- q point nominal latitude
- units :
- degrees_north
array(-0.06249997)
- zi(zi)float64-523.8 -481.0 -442.5 ... -2.5 -0.0
- axis :
- Z
- long_name :
- Interface pseudo-depth, -z*
- positive :
- up
- units :
- meter
array([-523.8 , -481.01, -442.51, -407.64, -375.88, -346.78, -319.99, -295.22, -272.22, -250.8 , -230.78, -212.02, -194.41, -177.85, -162.26, -147.57, -133.72, -120.66, -108.37, -96.83, -86.02, -75.94, -66.57, -57.91, -49.94, -42.66, -36.05, -30.1 , -24.81, -20.16, -16.15, -12.77, -10. , -7.5 , -5. , -2.5 , -0. ]) - zl(zl)float64-547.8 -502.4 ... -3.75 -1.25
- axis :
- Z
- long_name :
- Layer pseudo-depth, -z*
- positive :
- up
- units :
- meter
array([-547.75 , -502.405, -461.76 , -425.075, -391.76 , -361.33 , -333.385, -307.605, -283.72 , -261.51 , -240.79 , -221.4 , -203.215, -186.13 , -170.055, -154.915, -140.645, -127.19 , -114.515, -102.6 , -91.425, -80.98 , -71.255, -62.24 , -53.925, -46.3 , -39.355, -33.075, -27.455, -22.485, -18.155, -14.46 , -11.385, -8.75 , -6.25 , -3.75 , -1.25 ]) - eucmax(time)float64dask.array<chunksize=(8760,), meta=np.ndarray>
- units :
- m
- long_name :
- EUC maximum
- positive :
- up
Array Chunk Bytes 1.00 MiB 68.44 kiB Shape (131400,) (8760,) Dask graph 15 chunks in 21 graph layers Data type float64 numpy.ndarray - mldT(time)float64dask.array<chunksize=(8760,), meta=np.ndarray>
- long_name :
- MLD$_θ$
- units :
- m
- description :
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Array Chunk Bytes 1.00 MiB 68.44 kiB Shape (131400,) (8760,) Dask graph 15 chunks in 23 graph layers Data type float64 numpy.ndarray - dcl_mask(zi, time)booldask.array<chunksize=(37, 8760), meta=np.ndarray>
- description :
- True when 5m below mldT and above eucmax.
Array Chunk Bytes 4.64 MiB 316.52 kiB Shape (37, 131400) (37, 8760) Dask graph 15 chunks in 56 graph layers Data type bool numpy.ndarray - oni(time)float320.63 0.63 0.63 ... -0.87 -0.87
array([ 0.63, 0.63, 0.63, ..., -0.87, -0.87, -0.87], dtype=float32)
- en_mask(time)boolFalse False False ... False False
array([False, False, False, ..., False, False, False])
- ln_mask(time)boolFalse False False ... True True
array([False, False, False, ..., True, True, True])
- warm_mask(time)boolTrue True True ... True True True
array([ True, True, True, ..., True, True, True])
- cool_mask(time)boolFalse False False ... False False
array([False, False, False, ..., False, False, False])
- enso_transition(time)<U12'____________' ... 'La-Nina warm'
- description :
- Warner & Moum (2019) ENSO transition phase; El-Nino = ONI > 0.5 for at least 6 months; La-Nina = ONI < -0.5 for at least 6 months
array(['____________', '____________', '____________', ..., 'La-Nina warm', 'La-Nina warm', 'La-Nina warm'], dtype='<U12')
- KPP_BulkRi(time, zl)float32dask.array<chunksize=(8760, 37), meta=np.ndarray>
- cell_measures :
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- cell_methods :
- area:mean zl:mean yh:mean xh:mean time: mean
- long_name :
- Bulk Richardson number used to find the OBL depth used by [CVMix] KPP
- time_avg_info :
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- units :
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Array Chunk Bytes 18.55 MiB 1.24 MiB Shape (131400, 37) (8760, 37) Dask graph 15 chunks in 5 graph layers Data type float32 numpy.ndarray - KPP_NLtransport_heat(time, zi)float32dask.array<chunksize=(8760, 37), meta=np.ndarray>
- cell_measures :
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- cell_methods :
- area:mean zi:point yh:mean xh:mean time: mean
- long_name :
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- time_avg_info :
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- units :
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Array Chunk Bytes 18.55 MiB 1.24 MiB Shape (131400, 37) (8760, 37) Dask graph 15 chunks in 5 graph layers Data type float32 numpy.ndarray - KPP_OBLdepth(time)float32dask.array<chunksize=(131400,), meta=np.ndarray>
- cell_measures :
- area: areacello
- cell_methods :
- area:mean yh:mean xh:mean time: mean
- long_name :
- Thickness of the surface Ocean Boundary Layer calculated by [CVMix] KPP
- time_avg_info :
- average_T1,average_T2,average_DT
- units :
- meter
Array Chunk Bytes 513.28 kiB 513.28 kiB Shape (131400,) (131400,) Dask graph 1 chunks in 3 graph layers Data type float32 numpy.ndarray - KPP_buoyFlux(time, zi)float32dask.array<chunksize=(8760, 37), meta=np.ndarray>
- cell_measures :
- area: areacello
- cell_methods :
- area:mean zi:point yh:mean xh:mean time: mean
- long_name :
- Surface (and penetrating) buoyancy flux, as used by [CVMix] KPP
- time_avg_info :
- average_T1,average_T2,average_DT
- units :
- m2/s3
Array Chunk Bytes 18.55 MiB 1.24 MiB Shape (131400, 37) (8760, 37) Dask graph 15 chunks in 5 graph layers Data type float32 numpy.ndarray - KPP_ustar(time)float32dask.array<chunksize=(131400,), meta=np.ndarray>
- cell_measures :
- area: areacello
- cell_methods :
- area:mean yh:mean xh:mean time: mean
- long_name :
- Friction velocity, u*, as used by [CVMix] KPP
- time_avg_info :
- average_T1,average_T2,average_DT
- units :
- m/s
Array Chunk Bytes 513.28 kiB 513.28 kiB Shape (131400,) (131400,) Dask graph 1 chunks in 3 graph layers Data type float32 numpy.ndarray - KS_extra(time, zi)float32dask.array<chunksize=(8760, 37), meta=np.ndarray>
- cell_measures :
- area: areacello
- cell_methods :
- area:mean zi:point yh:mean xh:mean time: mean
- long_name :
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- time_avg_info :
- average_T1,average_T2,average_DT
- units :
- m2 s-1
Array Chunk Bytes 18.55 MiB 1.24 MiB Shape (131400, 37) (8760, 37) Dask graph 15 chunks in 5 graph layers Data type float32 numpy.ndarray - KT_extra(time, zi)float32dask.array<chunksize=(8760, 37), meta=np.ndarray>
- cell_measures :
- area: areacello
- cell_methods :
- area:mean zi:point yh:mean xh:mean time: mean
- long_name :
- Double-diffusive diffusivity for temperature
- time_avg_info :
- average_T1,average_T2,average_DT
- units :
- m2 s-1
Array Chunk Bytes 18.55 MiB 1.24 MiB Shape (131400, 37) (8760, 37) Dask graph 15 chunks in 5 graph layers Data type float32 numpy.ndarray - Kd_heat(time, zi)float32dask.array<chunksize=(8760, 37), meta=np.ndarray>
- cell_measures :
- area: areacello
- cell_methods :
- area:mean zi:point yh:mean xh:mean time: mean
- long_name :
- Total diapycnal diffusivity for heat at interfaces
- standard_name :
- ocean_vertical_heat_diffusivity
- time_avg_info :
- average_T1,average_T2,average_DT
- units :
- m2 s-1
Array Chunk Bytes 18.55 MiB 1.24 MiB Shape (131400, 37) (8760, 37) Dask graph 15 chunks in 5 graph layers Data type float32 numpy.ndarray - Kd_salt(time, zi)float32dask.array<chunksize=(8760, 37), meta=np.ndarray>
- cell_measures :
- area: areacello
- cell_methods :
- area:mean zi:point yh:mean xh:mean time: mean
- long_name :
- Total diapycnal diffusivity for salt at interfaces
- time_avg_info :
- average_T1,average_T2,average_DT
- units :
- m2 s-1
Array Chunk Bytes 18.55 MiB 1.24 MiB Shape (131400, 37) (8760, 37) Dask graph 15 chunks in 5 graph layers Data type float32 numpy.ndarray - Kv_u(time, zl)float32dask.array<chunksize=(8760, 37), meta=np.ndarray>
- cell_methods :
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- interp_method :
- none
- long_name :
- Total vertical viscosity at u-points
- standard_name :
- ocean_vertical_x_viscosity
- time_avg_info :
- average_T1,average_T2,average_DT
- units :
- m2 s-1
Array Chunk Bytes 18.55 MiB 1.24 MiB Shape (131400, 37) (8760, 37) Dask graph 15 chunks in 5 graph layers Data type float32 numpy.ndarray - Kv_v(time, zl)float32dask.array<chunksize=(8760, 37), meta=np.ndarray>
- cell_methods :
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- interp_method :
- none
- long_name :
- Total vertical viscosity at v-points
- standard_name :
- ocean_vertical_y_viscosity
- time_avg_info :
- average_T1,average_T2,average_DT
- units :
- m2 s-1
Array Chunk Bytes 18.55 MiB 1.24 MiB Shape (131400, 37) (8760, 37) Dask graph 15 chunks in 5 graph layers Data type float32 numpy.ndarray - N2(time, zi)float32dask.array<chunksize=(8760, 37), meta=np.ndarray>
- cell_measures :
- area: areacello
- cell_methods :
- area:mean zi:point yh:mean xh:mean time: mean
- long_name :
- Buoyancy frequency squared
- time_avg_info :
- average_T1,average_T2,average_DT
- units :
- s-2
Array Chunk Bytes 18.55 MiB 1.24 MiB Shape (131400, 37) (8760, 37) Dask graph 15 chunks in 5 graph layers Data type float32 numpy.ndarray - SSH(time)float32dask.array<chunksize=(131400,), meta=np.ndarray>
- cell_measures :
- area: areacello
- cell_methods :
- area:mean yh:mean xh:mean time: mean
- long_name :
- Sea Surface Height
- time_avg_info :
- average_T1,average_T2,average_DT
- units :
- m
Array Chunk Bytes 513.28 kiB 513.28 kiB Shape (131400,) (131400,) Dask graph 1 chunks in 3 graph layers Data type float32 numpy.ndarray - SW(time)float32dask.array<chunksize=(131400,), meta=np.ndarray>
- cell_measures :
- area: areacello
- cell_methods :
- area:mean yh:mean xh:mean time: mean
- long_name :
- Shortwave radiation flux into ocean
- standard_name :
- net_downward_shortwave_flux_at_sea_water_surface
- time_avg_info :
- average_T1,average_T2,average_DT
- units :
- W m-2
Array Chunk Bytes 513.28 kiB 513.28 kiB Shape (131400,) (131400,) Dask graph 1 chunks in 3 graph layers Data type float32 numpy.ndarray - SW_pen(time)float32dask.array<chunksize=(131400,), meta=np.ndarray>
- cell_measures :
- area: areacello
- cell_methods :
- area:mean yh:mean xh:mean time: mean
- long_name :
- Penetrating shortwave radiation flux into ocean
- time_avg_info :
- average_T1,average_T2,average_DT
- units :
- W m-2
Array Chunk Bytes 513.28 kiB 513.28 kiB Shape (131400,) (131400,) Dask graph 1 chunks in 3 graph layers Data type float32 numpy.ndarray - Tflx_dia_diff(time, zi)float32dask.array<chunksize=(8760, 37), meta=np.ndarray>
- cell_measures :
- area: areacello
- cell_methods :
- area:mean zi:point yh:mean xh:mean time: mean
- long_name :
- Diffusive diapycnal temperature flux across interfaces
- standard_name :
- ocean_vertical_diffusive_heat_flux
- time_avg_info :
- average_T1,average_T2,average_DT
- units :
- degC m s-1
Array Chunk Bytes 18.55 MiB 1.24 MiB Shape (131400, 37) (8760, 37) Dask graph 15 chunks in 5 graph layers Data type float32 numpy.ndarray - dens(time, zl)float32dask.array<chunksize=(8760, 37), meta=np.ndarray>
- cell_measures :
- volume: volcello area: areacello
- cell_methods :
- area:mean zl:mean yh:mean xh:mean time: mean
- long_name :
- Sea Water Salinity
- standard_name :
- sea_water_potential_density
- time_avg_info :
- average_T1,average_T2,average_DT
- units :
- kg/m^3
Array Chunk Bytes 18.55 MiB 1.24 MiB Shape (131400, 37) (8760, 37) Dask graph 15 chunks in 5 graph layers Data type float32 numpy.ndarray - densT(time, zl)float32dask.array<chunksize=(8760, 37), meta=np.ndarray>
- cell_measures :
- volume: volcello area: areacello
- cell_methods :
- area:mean zl:mean yh:mean xh:mean time: mean
- long_name :
- Sea Water Potential Temperature
- standard_name :
- sea_water_potential_density
- time_avg_info :
- average_T1,average_T2,average_DT
- units :
- kg/m3
Array Chunk Bytes 18.55 MiB 1.24 MiB Shape (131400, 37) (8760, 37) Dask graph 15 chunks in 5 graph layers Data type float32 numpy.ndarray - h(time, zl)float32dask.array<chunksize=(8760, 37), meta=np.ndarray>
- cell_measures :
- volume: volcello area: areacello
- cell_methods :
- area:mean zl:sum yh:mean xh:mean time: mean
- long_name :
- Layer Thickness
- time_avg_info :
- average_T1,average_T2,average_DT
- units :
- m
Array Chunk Bytes 18.55 MiB 1.24 MiB Shape (131400, 37) (8760, 37) Dask graph 15 chunks in 5 graph layers Data type float32 numpy.ndarray - mlotst(time)float32dask.array<chunksize=(131400,), meta=np.ndarray>
- cell_measures :
- area: areacello
- cell_methods :
- area:mean yh:mean xh:mean time: mean
- long_name :
- Ocean Mixed Layer Thickness Defined by Sigma T
- standard_name :
- ocean_mixed_layer_thickness_defined_by_sigma_t
- time_avg_info :
- average_T1,average_T2,average_DT
- units :
- m
Array Chunk Bytes 513.28 kiB 513.28 kiB Shape (131400,) (131400,) Dask graph 1 chunks in 3 graph layers Data type float32 numpy.ndarray - net_heat_surface(time)float32dask.array<chunksize=(131400,), meta=np.ndarray>
- cell_measures :
- area: areacello
- cell_methods :
- area:mean yh:mean xh:mean time: mean
- long_name :
- Surface ocean heat flux from SW+LW+lat+sens+mass transfer+frazil+restore+seaice_melt_heat or flux adjustments
- standard_name :
- surface_downward_heat_flux_in_sea_water
- time_avg_info :
- average_T1,average_T2,average_DT
- units :
- W m-2
Array Chunk Bytes 513.28 kiB 513.28 kiB Shape (131400,) (131400,) Dask graph 1 chunks in 3 graph layers Data type float32 numpy.ndarray - ri_grad_shear(time, zi)float32dask.array<chunksize=(8760, 37), meta=np.ndarray>
- cell_measures :
- area: areacello
- cell_methods :
- area:mean zi:point yh:mean xh:mean time: mean
- long_name :
- Gradient Richarson number used by MOM_CVMix_shear module
- time_avg_info :
- average_T1,average_T2,average_DT
- units :
- nondim
Array Chunk Bytes 18.55 MiB 1.24 MiB Shape (131400, 37) (8760, 37) Dask graph 15 chunks in 5 graph layers Data type float32 numpy.ndarray - ri_grad_shear_orig(time, zi)float32dask.array<chunksize=(8760, 37), meta=np.ndarray>
- cell_measures :
- area: areacello
- cell_methods :
- area:mean zi:point yh:mean xh:mean time: mean
- long_name :
- Original gradient Richarson number, before smoothing was applied. This is part of the MOM_CVMix_shear module and only available
- time_avg_info :
- average_T1,average_T2,average_DT
- units :
- nondim
Array Chunk Bytes 18.55 MiB 1.24 MiB Shape (131400, 37) (8760, 37) Dask graph 15 chunks in 5 graph layers Data type float32 numpy.ndarray - so(time, zl)float32dask.array<chunksize=(8760, 37), meta=np.ndarray>
- cell_measures :
- volume: volcello area: areacello
- cell_methods :
- area:mean zl:mean yh:mean xh:mean time: mean
- long_name :
- Sea Water Salinity
- standard_name :
- sea_water_salinity
- time_avg_info :
- average_T1,average_T2,average_DT
- units :
- psu
Array Chunk Bytes 18.55 MiB 1.24 MiB Shape (131400, 37) (8760, 37) Dask graph 15 chunks in 5 graph layers Data type float32 numpy.ndarray - taux(time)float32dask.array<chunksize=(131400,), meta=np.ndarray>
- cell_methods :
- yh:mean xq:point time: mean
- interp_method :
- none
- long_name :
- Zonal surface stress from ocean interactions with atmos and ice
- standard_name :
- surface_downward_x_stress
- time_avg_info :
- average_T1,average_T2,average_DT
- units :
- Pa
Array Chunk Bytes 513.28 kiB 513.28 kiB Shape (131400,) (131400,) Dask graph 1 chunks in 3 graph layers Data type float32 numpy.ndarray - tauy(time)float32dask.array<chunksize=(131400,), meta=np.ndarray>
- cell_methods :
- yq:point xh:mean time: mean
- interp_method :
- none
- long_name :
- Meridional surface stress ocean interactions with atmos and ice
- standard_name :
- surface_downward_y_stress
- time_avg_info :
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- units :
- Pa
Array Chunk Bytes 513.28 kiB 513.28 kiB Shape (131400,) (131400,) Dask graph 1 chunks in 3 graph layers Data type float32 numpy.ndarray - thetao(time, zl)float32dask.array<chunksize=(8760, 37), meta=np.ndarray>
- cell_measures :
- volume: volcello area: areacello
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- long_name :
- Sea Water Potential Temperature
- standard_name :
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- time_avg_info :
- average_T1,average_T2,average_DT
- units :
- degC
Array Chunk Bytes 18.55 MiB 1.24 MiB Shape (131400, 37) (8760, 37) Dask graph 15 chunks in 5 graph layers Data type float32 numpy.ndarray - uo(time, zl)float32dask.array<chunksize=(8760, 37), meta=np.ndarray>
- cell_methods :
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- interp_method :
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- long_name :
- Sea Water X Velocity
- standard_name :
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- time_avg_info :
- average_T1,average_T2,average_DT
- units :
- m s-1
Array Chunk Bytes 18.55 MiB 1.24 MiB Shape (131400, 37) (8760, 37) Dask graph 15 chunks in 5 graph layers Data type float32 numpy.ndarray - vo(time, zl)float32dask.array<chunksize=(8760, 37), meta=np.ndarray>
- cell_methods :
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- interp_method :
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- long_name :
- Sea Water Y Velocity
- standard_name :
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- time_avg_info :
- average_T1,average_T2,average_DT
- units :
- m s-1
Array Chunk Bytes 18.55 MiB 1.24 MiB Shape (131400, 37) (8760, 37) Dask graph 15 chunks in 5 graph layers Data type float32 numpy.ndarray - volcello(time, zl)float32dask.array<chunksize=(8760, 37), meta=np.ndarray>
- cell_methods :
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- long_name :
- Ocean grid-cell volume
- standard_name :
- ocean_volume
- time_avg_info :
- average_T1,average_T2,average_DT
- units :
- m3
Array Chunk Bytes 18.55 MiB 1.24 MiB Shape (131400, 37) (8760, 37) Dask graph 15 chunks in 5 graph layers Data type float32 numpy.ndarray - zos(time)float32dask.array<chunksize=(131400,), meta=np.ndarray>
- cell_measures :
- area: areacello
- cell_methods :
- area:mean yh:mean xh:mean time: mean
- long_name :
- Sea surface height above geoid
- standard_name :
- sea_surface_height_above_geoid
- time_avg_info :
- average_T1,average_T2,average_DT
- units :
- m
Array Chunk Bytes 513.28 kiB 513.28 kiB Shape (131400,) (131400,) Dask graph 1 chunks in 3 graph layers Data type float32 numpy.ndarray - α(time, zl)float32dask.array<chunksize=(8760, 37), meta=np.ndarray>
- standard_name :
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- units :
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Array Chunk Bytes 18.55 MiB 1.24 MiB Shape (131400, 37) (8760, 37) Dask graph 15 chunks in 5 graph layers Data type float32 numpy.ndarray - β(time, zl)float32dask.array<chunksize=(8760, 37), meta=np.ndarray>
- standard_name :
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- units :
- kg/g
Array Chunk Bytes 18.55 MiB 1.24 MiB Shape (131400, 37) (8760, 37) Dask graph 15 chunks in 5 graph layers Data type float32 numpy.ndarray - Tz(time, zi)float32dask.array<chunksize=(8760, 36), meta=np.ndarray>
- long_name :
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- units :
- Cm$^{-1}$
Array Chunk Bytes 18.55 MiB 1.20 MiB Shape (131400, 37) (8760, 36) Dask graph 30 chunks in 23 graph layers Data type float32 numpy.ndarray - Sz(time, zi)float32dask.array<chunksize=(8760, 36), meta=np.ndarray>
- long_name :
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- units :
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Array Chunk Bytes 18.55 MiB 1.20 MiB Shape (131400, 37) (8760, 36) Dask graph 30 chunks in 23 graph layers Data type float32 numpy.ndarray - N2T(time, zi)float32dask.array<chunksize=(8760, 36), meta=np.ndarray>
- long_name :
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- units :
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Array Chunk Bytes 18.55 MiB 1.20 MiB Shape (131400, 37) (8760, 36) Dask graph 30 chunks in 27 graph layers Data type float32 numpy.ndarray - S2(time, zi)float32dask.array<chunksize=(8760, 36), meta=np.ndarray>
- long_name :
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- units :
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Array Chunk Bytes 18.55 MiB 1.20 MiB Shape (131400, 37) (8760, 36) Dask graph 30 chunks in 50 graph layers Data type float32 numpy.ndarray - shred2(time, zi)float32dask.array<chunksize=(8760, 36), meta=np.ndarray>
- long_name :
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- units :
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Array Chunk Bytes 18.55 MiB 1.20 MiB Shape (131400, 37) (8760, 36) Dask graph 30 chunks in 66 graph layers Data type float32 numpy.ndarray - Rig_T(time, zi)float32dask.array<chunksize=(8760, 36), meta=np.ndarray>
- long_name :
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Array Chunk Bytes 18.55 MiB 1.20 MiB Shape (131400, 37) (8760, 36) Dask graph 30 chunks in 65 graph layers Data type float32 numpy.ndarray - Rig(time, zi)float32dask.array<chunksize=(8760, 36), meta=np.ndarray>
- cell_measures :
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- long_name :
- $Ri^g$
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Array Chunk Bytes 18.55 MiB 1.20 MiB Shape (131400, 37) (8760, 36) Dask graph 30 chunks in 55 graph layers Data type float32 numpy.ndarray - tau(time)float32dask.array<chunksize=(131400,), meta=np.ndarray>
- cell_methods :
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- interp_method :
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- long_name :
- Zonal surface stress from ocean interactions with atmos and ice
- time_avg_info :
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- units :
- Pa
Array Chunk Bytes 513.28 kiB 513.28 kiB Shape (131400,) (131400,) Dask graph 1 chunks in 9 graph layers Data type float32 numpy.ndarray - Jb(time, zi)float32dask.array<chunksize=(8760, 36), meta=np.ndarray>
- cell_measures :
- area: areacello
- cell_methods :
- area:mean zi:point yh:mean xh:mean time: mean
- long_name :
- Total diapycnal diffusivity for heat at interfaces
- standard_name :
- ocean_vertical_diffusive_buoyancy_flux
- time_avg_info :
- average_T1,average_T2,average_DT
- units :
- m2 s-1
Array Chunk Bytes 18.55 MiB 1.20 MiB Shape (131400, 37) (8760, 36) Dask graph 30 chunks in 62 graph layers Data type float32 numpy.ndarray - Jq(time, zi)float64dask.array<chunksize=(8760, 37), meta=np.ndarray>
- units :
- W/m^2
- long_name :
- $J_q^t$
Array Chunk Bytes 37.09 MiB 2.47 MiB Shape (131400, 37) (8760, 37) Dask graph 15 chunks in 6 graph layers Data type float64 numpy.ndarray - ν(time, zl)float32dask.array<chunksize=(8760, 37), meta=np.ndarray>
- cell_methods :
- zl:mean yh:mean xq:point time: mean
- interp_method :
- none
- long_name :
- Total vertical viscosity at u-points
- standard_name :
- ocean_vertical_momentum_diffusivity
- time_avg_info :
- average_T1,average_T2,average_DT
- units :
- m2 s-1
Array Chunk Bytes 18.55 MiB 1.24 MiB Shape (131400, 37) (8760, 37) Dask graph 15 chunks in 5 graph layers Data type float32 numpy.ndarray - shear_prod(time, zi)float32dask.array<chunksize=(8760, 36), meta=np.ndarray>
- long_name :
- $SP$
- units :
- W/kg
Array Chunk Bytes 18.55 MiB 1.20 MiB Shape (131400, 37) (8760, 36) Dask graph 30 chunks in 71 graph layers Data type float32 numpy.ndarray - eps(time, zi)float32dask.array<chunksize=(8760, 36), meta=np.ndarray>
- long_name :
- $ε$
- units :
- W/kg
Array Chunk Bytes 18.55 MiB 1.20 MiB Shape (131400, 37) (8760, 36) Dask graph 30 chunks in 121 graph layers Data type float32 numpy.ndarray - chi(time, zi)float32dask.array<chunksize=(8760, 36), meta=np.ndarray>
- long_name :
- $χ$
- units :
- C^2/s
Array Chunk Bytes 18.55 MiB 1.20 MiB Shape (131400, 37) (8760, 36) Dask graph 30 chunks in 30 graph layers Data type float32 numpy.ndarray - Rif(time, zi)float32dask.array<chunksize=(8760, 36), meta=np.ndarray>
- cell_measures :
- area: areacello
- cell_methods :
- area:mean zi:point yh:mean xh:mean time: mean
- long_name :
- Total diapycnal diffusivity for heat at interfaces
- standard_name :
- flux_richardson_number
- time_avg_info :
- average_T1,average_T2,average_DT
- units :
- m2 s-1
Array Chunk Bytes 18.55 MiB 1.20 MiB Shape (131400, 37) (8760, 36) Dask graph 30 chunks in 122 graph layers Data type float32 numpy.ndarray - sst(time)float32dask.array<chunksize=(8760,), meta=np.ndarray>
- cell_measures :
- volume: volcello area: areacello
- cell_methods :
- area:mean zl:mean yh:mean xh:mean time: mean
- long_name :
- $SST$
- standard_name :
- sea_surface_temperature
- time_avg_info :
- average_T1,average_T2,average_DT
- units :
- degC
Array Chunk Bytes 513.28 kiB 34.22 kiB Shape (131400,) (8760,) Dask graph 15 chunks in 5 graph layers Data type float32 numpy.ndarray
- title :
- KPP ν0=2.5, Ri0=0.5
<xarray.DatasetView> Dimensions: (time: 131400, zl: 37, zi: 37, nv: 2) Coordinates: (12/16) * nv (nv) float64 1.0 2.0 * time (time) datetime64[ns] 2003-01-01T00:30:00 ... 2017-... xh float64 -140.0 yh float64 0.0625 yq float64 -0.0625 * zi (zi) float64 -523.8 -481.0 -442.5 ... -5.0 -2.5 -0.0 ... ... oni (time) float32 0.63 0.63 0.63 ... -0.87 -0.87 -0.87 en_mask (time) bool False False False ... False False False ln_mask (time) bool False False False False ... True True True warm_mask (time) bool True True True True ... True True True cool_mask (time) bool False False False ... False False False enso_transition (time) <U12 '____________' ... 'La-Nina warm' Data variables: (12/49) KPP_BulkRi (time, zl) float32 dask.array<chunksize=(8760, 37), meta=np.ndarray> KPP_NLtransport_heat (time, zi) float32 dask.array<chunksize=(8760, 37), meta=np.ndarray> KPP_OBLdepth (time) float32 dask.array<chunksize=(131400,), meta=np.ndarray> KPP_buoyFlux (time, zi) float32 dask.array<chunksize=(8760, 37), meta=np.ndarray> KPP_ustar (time) float32 dask.array<chunksize=(131400,), meta=np.ndarray> KS_extra (time, zi) float32 dask.array<chunksize=(8760, 37), meta=np.ndarray> ... ... ν (time, zl) float32 dask.array<chunksize=(8760, 37), meta=np.ndarray> shear_prod (time, zi) float32 dask.array<chunksize=(8760, 36), meta=np.ndarray> eps (time, zi) float32 dask.array<chunksize=(8760, 36), meta=np.ndarray> chi (time, zi) float32 dask.array<chunksize=(8760, 36), meta=np.ndarray> Rif (time, zi) float32 dask.array<chunksize=(8760, 36), meta=np.ndarray> sst (time) float32 dask.array<chunksize=(8760,), meta=np.ndarray> Attributes: title: KPP ν0=2.5, Ri0=0.5new_baseline.kpp.lmd.005
N2-S2 histograms#
ref = tree["TAO"].ds.reset_coords(drop=True).cf.sel(Z=slice(-120, None))
counts = np.minimum(ref["S2"].cf.count("Z"), ref["N2T"].cf.count("Z")).load()
def calc_histograms(ds):
ds = ds.copy()
ds["tao_mask"] = counts.reindex(time=ds.time, method="nearest") > 5
ds["tao_mask"].attrs = {
"description": "True when there are more than 5 5-m T, u, v in TAO dataset"
}
# I did try masking out the model like the data
# ds = ds.where(ds.tao_mask)
return ds.update(mixpods.pdf_N2S2(ds))
tree = tree.map_over_subtree(calc_histograms)
daily composite#
dailies = tree.map_over_subtree(mixpods.daily_composites)
dailies
/glade/u/home/dcherian/miniconda3/envs/pump/lib/python3.10/site-packages/numpy/lib/nanfunctions.py:1217: RuntimeWarning: All-NaN slice encountered
r, k = function_base._ureduce(a, func=_nanmedian, axis=axis, out=out,
<xarray.DatasetView>
Dimensions: ()
Data variables:
*empty*- depth: 6
- hour: 24
- tau_bins: 3
- depth(depth)float64-89.0 -69.0 -59.0 -49.0 -39.0 -29.0
- axis :
- Z
- positive :
- up
- units :
- m
array([-89., -69., -59., -49., -39., -29.])
- latitude()float320.0
array(0., dtype=float32)
- longitude()float32-140.0
array(-140., dtype=float32)
- reference_pressure()int640
array(0)
- hour(hour)int640 1 2 3 4 5 6 ... 18 19 20 21 22 23
array([ 0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23]) - tau_bins(tau_bins)object(0.0, 0.04] ... (0.075, inf]
array([Interval(0.0, 0.04, closed='right'), Interval(0.04, 0.075, closed='right'), Interval(0.075, inf, closed='right')], dtype=object)
- KT(depth, hour, tau_bins)float649.959e-06 1.107e-05 ... 0.0001646
- standard_name :
- ocean_vertical_heat_diffusivity
array([[[9.95869314e-06, 1.10713814e-05, 1.81385305e-05], [7.62679606e-06, 9.84649659e-06, 1.98903482e-05], [8.26769194e-06, 1.03487366e-05, 1.69229065e-05], [5.09341517e-06, 1.06415264e-05, 9.94711354e-06], [6.27289392e-06, 1.01286448e-05, 9.09556433e-06], [5.85044689e-06, 9.09517149e-06, 1.06714061e-05], [5.92212630e-06, 8.40077865e-06, 1.11052356e-05], [6.47377658e-06, 9.78053302e-06, 1.13559910e-05], [5.77007820e-06, 1.00437481e-05, 1.54385405e-05], [5.60030739e-06, 8.32575368e-06, 1.31285365e-05], [5.27227053e-06, 7.15577324e-06, 1.44267402e-05], [5.03200713e-06, 9.93609875e-06, 1.68541630e-05], [6.42531180e-06, 1.04701955e-05, 1.45938509e-05], [5.45290240e-06, 1.06380336e-05, 1.94790247e-05], [5.05695470e-06, 1.08870254e-05, 2.13555496e-05], [6.78671892e-06, 1.23528905e-05, 2.01087241e-05], [9.80084606e-06, 1.38124457e-05, 2.85315836e-05], [6.90458131e-06, 1.42042099e-05, 2.59700678e-05], [8.44895131e-06, 1.38211283e-05, 2.92244979e-05], [6.96328801e-06, 1.80749482e-05, 2.81233184e-05], ... [2.49796366e-05, 1.51222154e-04, 1.54133302e-03], [2.54375198e-05, 1.92556637e-04, 1.98323078e-03], [2.95016029e-05, 2.74405783e-04, 2.23706350e-03], [2.91381387e-05, 5.29042782e-04, 2.46231496e-03], [3.22679193e-05, 6.25229486e-04, 2.36063586e-03], [4.87863828e-05, 6.82247064e-04, 2.44611497e-03], [7.65505437e-05, 8.72437582e-04, 2.08043411e-03], [9.60910794e-05, 7.32102430e-04, 2.33006858e-03], [8.45055247e-05, 8.06508643e-04, 1.81524742e-03], [9.68881747e-05, 7.19295971e-04, 2.07390795e-03], [1.12205990e-04, 8.31837440e-04, 1.82439093e-03], [1.25092879e-04, 8.00568096e-04, 1.85697065e-03], [1.41021393e-04, 7.84944210e-04, 1.71427588e-03], [1.35184262e-04, 7.04609354e-04, 1.41404584e-03], [1.12211968e-04, 3.97215676e-04, 1.07973119e-03], [8.04180511e-05, 2.89701497e-04, 6.65654642e-04], [6.64714222e-05, 1.94172831e-04, 4.08622947e-04], [5.75134301e-05, 1.40309406e-04, 2.67942773e-04], [5.13853203e-05, 1.01548040e-04, 1.64550217e-04], [4.10933487e-05, 8.66966898e-05, 1.64556856e-04]]]) - eps(depth, hour, tau_bins)float644.205e-09 6.427e-09 ... 1.608e-08
array([[[4.20484047e-09, 6.42665336e-09, 1.03495228e-08], [3.39942155e-09, 6.09951608e-09, 9.95406422e-09], [3.22205779e-09, 6.00608012e-09, 9.89785322e-09], [2.43384740e-09, 5.65188162e-09, 6.43540665e-09], [2.34056719e-09, 4.68248253e-09, 5.86884639e-09], [2.42516755e-09, 4.36594516e-09, 6.04769734e-09], [2.01203910e-09, 4.50894201e-09, 6.80387559e-09], [2.77474401e-09, 4.06819005e-09, 5.78686391e-09], [3.02695691e-09, 4.19429197e-09, 9.25259960e-09], [2.16803642e-09, 4.07562895e-09, 7.99217582e-09], [2.25024698e-09, 3.86947366e-09, 1.08315576e-08], [2.34519609e-09, 4.58983072e-09, 8.80408647e-09], [2.43251603e-09, 5.04441870e-09, 1.00646850e-08], [2.87316856e-09, 4.50655083e-09, 1.06291960e-08], [3.21955035e-09, 5.40452670e-09, 1.13614463e-08], [2.93556264e-09, 5.97819732e-09, 1.35444442e-08], [3.32274328e-09, 6.36330330e-09, 1.69035479e-08], [3.41472292e-09, 7.45018817e-09, 1.40619889e-08], [4.16563716e-09, 6.50261284e-09, 1.49772749e-08], [3.93711329e-09, 8.03877507e-09, 1.71921395e-08], ... [4.32062308e-09, 2.14336097e-08, 1.84017958e-07], [4.32244969e-09, 3.23795526e-08, 1.77984766e-07], [4.99443371e-09, 4.76849714e-08, 1.82287235e-07], [6.67475746e-09, 7.43192793e-08, 1.80947783e-07], [7.69190689e-09, 8.48603912e-08, 1.83042578e-07], [1.24068720e-08, 8.70767941e-08, 1.63145058e-07], [1.75400970e-08, 9.81732289e-08, 1.55381279e-07], [2.07108437e-08, 8.34432489e-08, 1.47163501e-07], [2.09570176e-08, 9.55806383e-08, 1.28104332e-07], [2.49357959e-08, 7.48081255e-08, 1.38691872e-07], [2.02488017e-08, 7.54215709e-08, 1.17639124e-07], [2.72920187e-08, 7.42685031e-08, 1.22579504e-07], [2.84401490e-08, 7.54687544e-08, 1.20862216e-07], [2.32893736e-08, 6.57975258e-08, 9.00575173e-08], [1.80033299e-08, 4.22211119e-08, 6.23893426e-08], [1.57912073e-08, 2.80175873e-08, 4.02353834e-08], [1.23950854e-08, 2.20880606e-08, 2.95446015e-08], [1.11499973e-08, 1.59336048e-08, 1.98472383e-08], [8.36356549e-09, 1.27149248e-08, 1.63302630e-08], [7.16264221e-09, 1.10760452e-08, 1.60790152e-08]]]) - chi(depth, hour, tau_bins)float641.67e-08 3.739e-08 ... 1.95e-08
array([[[1.67021892e-08, 3.73895360e-08, 7.93282652e-08], [1.45695729e-08, 3.21540306e-08, 7.51363908e-08], [1.66509708e-08, 3.17736964e-08, 7.22932589e-08], [1.00107661e-08, 3.13534986e-08, 4.77883670e-08], [1.33179056e-08, 2.40084728e-08, 3.57552816e-08], [9.22712244e-09, 2.50863744e-08, 4.33211842e-08], [8.08744573e-09, 2.38605289e-08, 3.82013297e-08], [1.09612925e-08, 2.27882209e-08, 3.99283958e-08], [1.34920747e-08, 1.97982703e-08, 4.72313840e-08], [9.85650711e-09, 2.04048889e-08, 5.14985775e-08], [1.08937936e-08, 2.35725432e-08, 6.87134261e-08], [1.21212706e-08, 2.51579652e-08, 7.15330504e-08], [1.32546719e-08, 3.39587720e-08, 9.47722781e-08], [1.69953873e-08, 2.60973815e-08, 7.29116184e-08], [1.67531863e-08, 3.07021553e-08, 6.79356643e-08], [1.77120188e-08, 4.09737308e-08, 7.29900722e-08], [1.93716942e-08, 4.31379369e-08, 9.26064400e-08], [2.09236218e-08, 4.34670007e-08, 9.19779817e-08], [2.48015149e-08, 3.95476993e-08, 8.28554984e-08], [2.05587984e-08, 5.27063058e-08, 8.39105570e-08], ... [6.58562963e-09, 4.00100193e-08, 1.82930124e-07], [8.27051334e-09, 5.94085776e-08, 1.62815748e-07], [1.05461194e-08, 7.08934926e-08, 1.68249950e-07], [1.42336592e-08, 9.11751999e-08, 1.81170217e-07], [1.72444375e-08, 9.91038146e-08, 1.50109762e-07], [2.48941347e-08, 1.02137827e-07, 1.58365512e-07], [3.49353545e-08, 1.15828778e-07, 1.27671006e-07], [4.11614687e-08, 9.85426121e-08, 1.36028494e-07], [3.54397051e-08, 9.90287508e-08, 1.15470101e-07], [4.15427310e-08, 8.94060867e-08, 1.12016269e-07], [3.92166332e-08, 8.64900353e-08, 9.95322983e-08], [5.45326891e-08, 8.52935771e-08, 1.10918666e-07], [4.42275054e-08, 7.57406371e-08, 9.45690680e-08], [4.10326472e-08, 7.06337841e-08, 7.52282802e-08], [3.53795830e-08, 4.58133432e-08, 5.44219219e-08], [2.37814091e-08, 3.15729107e-08, 3.78253266e-08], [1.95929775e-08, 2.64875960e-08, 3.30521637e-08], [1.66729435e-08, 2.15147840e-08, 2.22266528e-08], [1.50428825e-08, 1.53505017e-08, 1.73559861e-08], [1.08272541e-08, 1.40830926e-08, 1.94961160e-08]]]) - Jb(depth, hour, tau_bins)float64nan nan nan nan ... nan nan nan nan
- standard_name :
- ocean_vertical_diffusive_buoyancy_flux
array([[[nan, nan, nan], [nan, nan, nan], [nan, nan, nan], [nan, nan, nan], [nan, nan, nan], [nan, nan, nan], [nan, nan, nan], [nan, nan, nan], [nan, nan, nan], [nan, nan, nan], [nan, nan, nan], [nan, nan, nan], [nan, nan, nan], [nan, nan, nan], [nan, nan, nan], [nan, nan, nan], [nan, nan, nan], [nan, nan, nan], [nan, nan, nan], [nan, nan, nan], ... [nan, nan, nan], [nan, nan, nan], [nan, nan, nan], [nan, nan, nan], [nan, nan, nan], [nan, nan, nan], [nan, nan, nan], [nan, nan, nan], [nan, nan, nan], [nan, nan, nan], [nan, nan, nan], [nan, nan, nan], [nan, nan, nan], [nan, nan, nan], [nan, nan, nan], [nan, nan, nan], [nan, nan, nan], [nan, nan, nan], [nan, nan, nan], [nan, nan, nan]]]) - Jq(depth, hour, tau_bins)float64-1.003 -1.842 ... -2.971 -4.428
array([[[ -1.00256446, -1.84244745, -2.80850936], [ -0.91887084, -1.62464068, -2.56282757], [ -0.88374135, -1.58679603, -2.88823456], [ -0.70443327, -1.6103817 , -1.77298658], [ -0.68665724, -1.27295597, -1.77228 ], [ -0.66664796, -1.19898684, -1.67626741], [ -0.59730391, -1.23465212, -1.70404798], [ -0.7235757 , -1.18544042, -1.59040206], [ -0.75019594, -1.04231212, -2.57469427], [ -0.57483421, -1.14463015, -2.24184293], [ -0.63752255, -1.10632234, -2.90389878], [ -0.65130446, -1.26093457, -2.35183439], [ -0.66722366, -1.3861301 , -2.72893158], [ -0.81851688, -1.24810229, -2.85460253], [ -0.72343038, -1.27913762, -2.93287239], [ -0.82057933, -1.73613097, -3.35338815], [ -0.92375014, -1.80837082, -4.44104197], [ -0.87323403, -2.01851581, -3.77664889], [ -1.19708228, -1.7267686 , -4.23530912], [ -0.98020427, -2.19262619, -4.13656709], ... [ -1.11297409, -5.84520527, -47.88722398], [ -1.20329183, -9.40240756, -45.37124552], [ -1.33200184, -12.95762863, -46.64407742], [ -1.73752661, -19.31055455, -45.85240017], [ -2.00999545, -20.92691833, -45.36418532], [ -3.38542206, -21.6217159 , -41.41348273], [ -4.660082 , -25.72382479, -37.29098972], [ -5.53276281, -20.1004968 , -36.00067158], [ -5.56787429, -22.40393839, -31.23276616], [ -5.82381354, -19.36849336, -34.99038496], [ -5.01723603, -19.13635109, -28.69033267], [ -6.73045836, -19.73596063, -28.18919034], [ -7.09852648, -18.76319064, -30.2266072 ], [ -6.12788 , -16.48818028, -21.37988398], [ -4.62785644, -10.76205284, -16.19281641], [ -4.21420556, -7.65540787, -10.55800383], [ -3.29425731, -5.96463463, -7.87434395], [ -2.86694385, -4.28650835, -5.42953264], [ -2.33320233, -3.38921404, -4.47756404], [ -1.82422195, -2.97098078, -4.42772743]]]) - S2(depth, hour, tau_bins)float320.0001345 0.0002001 ... 0.0001682
- long_name :
- $S^2$
array([[[0.00013448, 0.00020006, 0.00030025], [0.00013459, 0.00019925, 0.00030133], [0.00013651, 0.00019142, 0.0003029 ], [0.00012919, 0.00020452, 0.00029948], [0.00013896, 0.0002124 , 0.00030397], [0.00015213, 0.00019731, 0.00031407], [0.00014794, 0.00019219, 0.00030202], [0.00015217, 0.00019176, 0.00030711], [0.0001403 , 0.00019476, 0.00029934], [0.00013978, 0.00018397, 0.00030806], [0.00014281, 0.00018728, 0.00030986], [0.00014091, 0.00018794, 0.00031204], [0.00013823, 0.0001874 , 0.000305 ], [0.00013669, 0.00019039, 0.00031266], [0.00013547, 0.00019014, 0.00030768], [0.0001324 , 0.00018962, 0.00029873], [0.00013033, 0.00018335, 0.00029489], [0.00013093, 0.0001916 , 0.00030148], [0.00014072, 0.00018797, 0.00031962], [0.00014378, 0.00018233, 0.00031395], ... [0.00030681, 0.00022737, 0.00020181], [0.00031573, 0.00023622, 0.000205 ], [0.00032313, 0.0002395 , 0.00019791], [0.00032533, 0.00024731, 0.00017745], [0.0003447 , 0.00024576, 0.00016273], [0.00036876, 0.0002621 , 0.00016414], [0.0003664 , 0.000238 , 0.00016017], [0.00036569, 0.00023603, 0.00015653], [0.00035129, 0.00023055, 0.00014936], [0.00035962, 0.00021455, 0.00015314], [0.00033461, 0.00022228, 0.00015203], [0.00038251, 0.00024297, 0.00017307], [0.00040409, 0.000252 , 0.0001731 ], [0.00037549, 0.00023915, 0.00015291], [0.00036383, 0.00021962, 0.0001535 ], [0.00036171, 0.00020565, 0.0001415 ], [0.0003339 , 0.00020438, 0.00015409], [0.00035724, 0.00019812, 0.00015214], [0.0003461 , 0.00020423, 0.00015108], [0.0003532 , 0.00020651, 0.00016817]]], dtype=float32) - N2(depth, hour, tau_bins)float640.0002082 0.0002229 ... 2.895e-05
- long_name :
- $N^2$
array([[[2.08151738e-04, 2.22862104e-04, 2.14441416e-04], [2.16603991e-04, 2.24483495e-04, 2.21728203e-04], [2.15355923e-04, 2.21838325e-04, 2.18587475e-04], [2.15544742e-04, 2.22885541e-04, 2.12326524e-04], [2.16644011e-04, 2.13538050e-04, 2.23611350e-04], [2.09853593e-04, 2.20879385e-04, 2.19642997e-04], [1.98066216e-04, 2.21105441e-04, 2.18486263e-04], [2.16155790e-04, 2.18854377e-04, 2.23897831e-04], [2.21096656e-04, 2.18472571e-04, 2.21506161e-04], [2.16910676e-04, 2.20828730e-04, 2.15561007e-04], [2.20007830e-04, 2.25418650e-04, 2.14505446e-04], [2.13170636e-04, 2.18209400e-04, 2.26985607e-04], [2.08379942e-04, 2.20359529e-04, 2.28006372e-04], [2.18869956e-04, 2.12548021e-04, 2.37007497e-04], [2.22657337e-04, 2.18700017e-04, 2.19859678e-04], [2.16105074e-04, 2.18497812e-04, 2.29437082e-04], [2.15577658e-04, 2.11075774e-04, 2.20386303e-04], [2.14909841e-04, 2.19245199e-04, 2.10944560e-04], [2.18744613e-04, 2.23280408e-04, 2.02637232e-04], [2.18981413e-04, 2.22255352e-04, 2.07395227e-04], ... [6.96024719e-05, 4.77216368e-05, 3.52536021e-05], [7.21013450e-05, 4.76598289e-05, 3.29697584e-05], [7.22574894e-05, 5.01282745e-05, 3.12980790e-05], [7.68367868e-05, 4.81516730e-05, 3.00344601e-05], [7.68827915e-05, 4.78022934e-05, 2.65751683e-05], [7.66054714e-05, 4.80892891e-05, 2.51018158e-05], [7.63678736e-05, 4.39940175e-05, 2.65891577e-05], [7.64221137e-05, 4.27375694e-05, 2.65801164e-05], [7.63855405e-05, 4.13222946e-05, 2.54512380e-05], [7.23437008e-05, 3.95318950e-05, 2.40070337e-05], [7.32406023e-05, 3.87474723e-05, 2.17285776e-05], [7.84975393e-05, 3.86002397e-05, 2.22512416e-05], [7.96900786e-05, 3.91479868e-05, 2.12247197e-05], [7.60129546e-05, 3.42026320e-05, 2.17803309e-05], [7.44090646e-05, 3.69594533e-05, 2.22136917e-05], [7.49782129e-05, 3.66955626e-05, 2.24673463e-05], [7.37285223e-05, 3.66268637e-05, 2.49871805e-05], [7.33997174e-05, 3.74016674e-05, 2.55136179e-05], [7.08907159e-05, 4.04018172e-05, 2.61858948e-05], [6.88821220e-05, 4.19587334e-05, 2.89518715e-05]]]) - Rig(depth, hour, tau_bins)float641.406 1.252 ... 0.07694 0.05421
- long_name :
- $Ri^g$
array([[[1.40644738, 1.25178115, 0.74488556], [1.41762533, 1.35060308, 0.76782803], [1.47274045, 1.28796047, 0.71477707], [1.77206894, 1.23581944, 0.85990554], [1.42856916, 1.18887077, 0.86747588], [1.37627981, 1.250239 , 0.79682319], [1.35225077, 1.14314482, 0.89240256], [1.53488295, 1.13362321, 0.81762861], [1.43149522, 1.18854333, 0.82108334], [1.69124541, 1.31780546, 0.74364754], [1.63997589, 1.2650186 , 0.75719731], [1.60196071, 1.29321876, 0.80676174], [1.76211032, 1.29848346, 0.82638659], [1.89364181, 1.2401134 , 0.81697784], [1.75861073, 1.2281106 , 0.82555718], [1.79334254, 1.25421119, 0.87983679], [1.75460255, 1.23742487, 0.81284393], [1.6443649 , 1.20305246, 0.80500277], [1.90235508, 1.39715558, 0.75862198], [1.59556172, 1.27929263, 0.69249317], ... [0.11922037, 0.08834196, 0.05390135], [0.11364714, 0.0985151 , 0.04969669], [0.11955433, 0.09013164, 0.04259646], [0.13152116, 0.08148522, 0.04038412], [0.12462708, 0.07752266, 0.0412092 ], [0.1059685 , 0.08015321, 0.03483511], [0.11336615, 0.07559725, 0.03430897], [0.10954652, 0.07044978, 0.03097055], [0.10013768, 0.07146289, 0.03221148], [0.08570449, 0.07030557, 0.0349513 ], [0.09017043, 0.06546468, 0.03168679], [0.08669634, 0.0582093 , 0.03019886], [0.09715547, 0.06009826, 0.02640776], [0.07935822, 0.05776852, 0.03262731], [0.09296572, 0.05099493, 0.03820643], [0.08410832, 0.06245423, 0.04184208], [0.10067066, 0.0679023 , 0.04833413], [0.10772209, 0.06960988, 0.05154636], [0.096249 , 0.07780057, 0.05655469], [0.10424006, 0.07694161, 0.05421351]]]) - Rig_T(depth, hour, tau_bins)float641.901 1.204 0.6325 ... 0.12 0.1185
- long_name :
- $Ri^g_T$
array([[[1.90144926, 1.20402004, 0.63250394], [1.97722436, 1.16803555, 0.63929729], [1.82363029, 1.19271362, 0.63758984], [2.00858352, 1.17413288, 0.64556353], [1.764249 , 1.08857607, 0.63981687], [1.6722678 , 1.14029434, 0.63841363], [1.64416596, 1.07404686, 0.69016643], [1.80985361, 1.1286188 , 0.66627749], [1.84750455, 1.11619713, 0.65720696], [1.85832509, 1.14625708, 0.64646297], [1.82383741, 1.28825209, 0.60547409], [1.90534517, 1.22815954, 0.65631865], [1.92757436, 1.17589125, 0.6729475 ], [1.95646798, 1.15944217, 0.67811545], [2.15228629, 1.2346199 , 0.68925813], [2.17114092, 1.25440367, 0.67767255], [1.9283005 , 1.25106341, 0.65996391], [2.09583235, 1.16882993, 0.64695664], [2.00001933, 1.18052973, 0.63612982], [1.95096458, 1.24909609, 0.60900943], ... [0.14035465, 0.15645461, 0.13741426], [0.15093877, 0.1540195 , 0.12269896], [0.1517013 , 0.14701272, 0.10974087], [0.15332836, 0.13742055, 0.10176559], [0.14785674, 0.1249745 , 0.09094855], [0.14468148, 0.11747535, 0.08905629], [0.1371656 , 0.11502819, 0.08797114], [0.13831776, 0.10755145, 0.08060554], [0.1255792 , 0.1066724 , 0.07354782], [0.11734482, 0.09774099, 0.07285993], [0.12316585, 0.0914999 , 0.07129744], [0.11004655, 0.0806289 , 0.05700402], [0.12113939, 0.07903602, 0.04899825], [0.11156332, 0.08470378, 0.06233322], [0.11291802, 0.09061568, 0.07497081], [0.12169541, 0.09945144, 0.08995757], [0.12816946, 0.11179433, 0.0962672 ], [0.13384635, 0.11637368, 0.11296923], [0.12542545, 0.12154479, 0.11954271], [0.12356876, 0.12001706, 0.11848762]]]) - tau(hour, tau_bins)float640.02709 0.05733 ... 0.05745 0.09308
array([[0.0270877 , 0.05732647, 0.09203958], [0.02680315, 0.05759896, 0.09214987], [0.0266931 , 0.05764483, 0.09244683], [0.02699333, 0.05713952, 0.09263131], [0.02688743, 0.0573335 , 0.09261309], [0.02682686, 0.05738899, 0.0923755 ], [0.02736078, 0.05730989, 0.09163058], [0.02709459, 0.05719675, 0.09233963], [0.0260143 , 0.05757715, 0.09312405], [0.02643325, 0.05748537, 0.09276128], [0.02604512, 0.05695688, 0.09281864], [0.02653221, 0.05750187, 0.09254788], [0.02541264, 0.05698433, 0.09270642], [0.0258142 , 0.0573662 , 0.09281572], [0.02577848, 0.05747551, 0.09283653], [0.02582663, 0.05766199, 0.09305366], [0.0262083 , 0.05818581, 0.09442765], [0.02586172, 0.05809102, 0.0939246 ], [0.02600226, 0.05773976, 0.09318213], [0.02695185, 0.05763241, 0.09303027], [0.02669914, 0.05756156, 0.09276316], [0.02679048, 0.05723235, 0.09331579], [0.02705673, 0.05729915, 0.09280436], [0.02745168, 0.05745369, 0.09308222]])
- CREATION_DATE :
- 23:26 24-FEB-2021
- Data_Source :
- Global Tropical Moored Buoy Array Project Office/NOAA/PMEL
- File_info :
- Contact: Dai.C.McClurg@noaa.gov
- Request_for_acknowledgement :
- If you use these data in publications or presentations, please acknowledge the GTMBA Project Office of NOAA/PMEL. Also, we would appreciate receiving a preprint and/or reprint of publications utilizing the data for inclusion in our bibliography. Relevant publications should be sent to: GTMBA Project Office, NOAA/Pacific Marine Environmental Laboratory, 7600 Sand Point Way NE, Seattle, WA 98115
- _FillValue :
- 1.0000000409184788e+35
- array :
- TAO/TRITON
- missing_value :
- 1.0000000409184788e+35
- platform_code :
- 0n165e
- site_code :
- 0n165e
- wmo_platform_code :
- 52321
<xarray.DatasetView> Dimensions: (depth: 6, hour: 24, tau_bins: 3) Coordinates: * depth (depth) float64 -89.0 -69.0 -59.0 -49.0 -39.0 -29.0 latitude float32 0.0 longitude float32 -140.0 reference_pressure int64 0 * hour (hour) int64 0 1 2 3 4 5 6 7 ... 16 17 18 19 20 21 22 23 * tau_bins (tau_bins) object (0.0, 0.04] (0.04, 0.075] (0.075, inf] Data variables: KT (depth, hour, tau_bins) float64 9.959e-06 ... 0.0001646 eps (depth, hour, tau_bins) float64 4.205e-09 ... 1.608e-08 chi (depth, hour, tau_bins) float64 1.67e-08 ... 1.95e-08 Jb (depth, hour, tau_bins) float64 nan nan nan ... nan nan Jq (depth, hour, tau_bins) float64 -1.003 -1.842 ... -4.428 S2 (depth, hour, tau_bins) float32 0.0001345 ... 0.0001682 N2 (depth, hour, tau_bins) float64 0.0002082 ... 2.895e-05 Rig (depth, hour, tau_bins) float64 1.406 1.252 ... 0.05421 Rig_T (depth, hour, tau_bins) float64 1.901 1.204 ... 0.1185 tau (hour, tau_bins) float64 0.02709 0.05733 ... 0.09308 Attributes: CREATION_DATE: 23:26 24-FEB-2021 Data_Source: Global Tropical Moored Buoy Array Project O... File_info: Contact: Dai.C.McClurg@noaa.gov Request_for_acknowledgement: If you use these data in publications or pr... _FillValue: 1.0000000409184788e+35 array: TAO/TRITON missing_value: 1.0000000409184788e+35 platform_code: 0n165e site_code: 0n165e wmo_platform_code: 52321TAO- depth: 6
- hour: 24
- tau_bins: 3
- depth(depth)float64-89.0 -69.0 -59.0 -49.0 -39.0 -29.0
- cartesian_axis :
- Z
- long_name :
- Interface pseudo-depth, -z*
- positive :
- up
- units :
- meter
array([-89., -69., -59., -49., -39., -29.])
- xh()float64-140.0
- cartesian_axis :
- X
- domain_decomposition :
- [220, 222, 220, 221]
- long_name :
- h point nominal longitude
- units :
- degrees_east
array(-140.)
- yh()float640.0625
- cartesian_axis :
- Y
- domain_decomposition :
- [210, 258, 210, 221]
- long_name :
- h point nominal latitude
- units :
- degrees_north
array(0.06249997)
- yq()float64-0.0625
- cartesian_axis :
- Y
- domain_decomposition :
- [209, 257, 209, 221]
- long_name :
- q point nominal latitude
- units :
- degrees_north
array(-0.06249997)
- hour(hour)int640 1 2 3 4 5 6 ... 18 19 20 21 22 23
array([ 0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23]) - tau_bins(tau_bins)object(0.0, 0.04] ... (0.075, inf]
- cell_methods :
- yh:mean xq:point time: mean
- interp_method :
- none
- long_name :
- Zonal surface stress from ocean interactions with atmos and ice
- time_avg_info :
- average_T1,average_T2,average_DT
- units :
- Pa
array([Interval(0.0, 0.04, closed='right'), Interval(0.04, 0.075, closed='right'), Interval(0.075, inf, closed='right')], dtype=object)
- KT(depth, hour, tau_bins)float321.001e-06 1.001e-06 ... 0.0006551
- cell_measures :
- area: areacello
- cell_methods :
- area:mean zi:point yh:mean xh:mean time: mean
- long_name :
- Total diapycnal diffusivity for heat at interfaces
- time_avg_info :
- average_T1,average_T2,average_DT
- units :
- m2 s-1
- standard_name :
- ocean_vertical_heat_diffusivity
array([[[1.00062630e-06, 1.00062675e-06, 1.00062948e-06], [1.00062630e-06, 1.00062675e-06, 1.00062914e-06], [1.00062630e-06, 1.00062664e-06, 1.00070815e-06], [1.00062630e-06, 1.00062675e-06, 1.00062823e-06], [1.00062630e-06, 1.00062664e-06, 1.00062800e-06], [1.00062630e-06, 1.00062664e-06, 1.00062800e-06], [1.00062630e-06, 1.00062664e-06, 1.00062812e-06], [1.00062630e-06, 1.00062664e-06, 1.00062857e-06], [1.00062630e-06, 1.00062664e-06, 1.00062869e-06], [1.00062630e-06, 1.00062664e-06, 1.00062857e-06], [1.00062630e-06, 1.00062664e-06, 1.00570742e-06], [1.00062630e-06, 1.00062664e-06, 1.00063801e-06], [1.00062630e-06, 1.00062664e-06, 1.00505042e-06], [1.00062630e-06, 1.00062664e-06, 1.00063016e-06], [1.00062630e-06, 1.00062664e-06, 1.00062857e-06], [1.00062630e-06, 1.00062653e-06, 1.00062778e-06], [1.00062630e-06, 1.00062664e-06, 1.00062721e-06], [1.00062630e-06, 1.00062653e-06, 1.00062732e-06], [1.00062630e-06, 1.00062653e-06, 1.00062744e-06], [1.00062618e-06, 1.00062664e-06, 1.00062789e-06], ... [1.31017505e-03, 1.84910290e-03, 3.04656965e-03], [1.20016653e-03, 1.93014904e-03, 2.43997667e-02], [1.26303639e-03, 2.55707884e-03, 3.35960016e-02], [1.21755805e-03, 3.45059624e-03, 4.70925234e-02], [1.20411417e-03, 6.63228473e-03, 5.48477545e-02], [1.20652630e-03, 1.19018322e-02, 6.12227395e-02], [1.33580097e-03, 1.68863572e-02, 6.64590597e-02], [1.38179970e-03, 2.08421741e-02, 7.17242733e-02], [1.61000830e-03, 2.49606222e-02, 7.40951598e-02], [1.71344716e-03, 2.64077000e-02, 7.43862391e-02], [1.82845863e-03, 2.77032442e-02, 7.27289319e-02], [1.99175603e-03, 2.91718896e-02, 7.13082328e-02], [1.64016616e-03, 1.40310498e-02, 5.11850938e-02], [2.00258894e-03, 2.12116283e-03, 1.37765761e-02], [1.67123857e-03, 1.55570242e-03, 4.48137894e-03], [1.43854087e-03, 6.79666526e-04, 3.39539582e-03], [2.04196898e-03, 1.61034847e-03, 7.48192775e-04], [2.00651051e-03, 1.96600612e-03, 5.42554015e-04], [1.92917790e-03, 1.86887372e-03, 4.04946972e-04], [1.80337648e-03, 1.76201062e-03, 6.55124430e-04]]], dtype=float32) - eps(depth, hour, tau_bins)float323.001e-08 1.736e-07 ... 3.2e-08
- long_name :
- $ε$
- units :
- W/kg
array([[[3.00083727e-08, 1.73550305e-07, 3.20981485e-07], [2.98638838e-08, 1.78816521e-07, 3.11246595e-07], [3.06492360e-08, 1.81410115e-07, 3.16398513e-07], [3.16908242e-08, 1.81652055e-07, 2.98138332e-07], [3.11788320e-08, 1.74377433e-07, 2.96343785e-07], [2.98984233e-08, 1.70588990e-07, 3.04399180e-07], [2.91050224e-08, 1.61966597e-07, 2.92950801e-07], [2.95839531e-08, 1.59746861e-07, 2.87097237e-07], [3.12666408e-08, 1.56512073e-07, 2.81551166e-07], [3.21704050e-08, 1.55229969e-07, 2.67078150e-07], [3.19440652e-08, 1.50670360e-07, 2.74645004e-07], [3.20376188e-08, 1.50261016e-07, 2.74898809e-07], [3.24038005e-08, 1.48318989e-07, 2.63479620e-07], [3.08092964e-08, 1.40655715e-07, 2.57241481e-07], [2.95377269e-08, 1.37034775e-07, 2.56086707e-07], [2.93052338e-08, 1.29154699e-07, 2.41482866e-07], [2.80925558e-08, 1.29672216e-07, 2.39818291e-07], [2.65684719e-08, 1.28819224e-07, 2.57427473e-07], [2.40848834e-08, 1.30246178e-07, 2.71984561e-07], [2.53702339e-08, 1.34602928e-07, 3.02511069e-07], ... [1.38809497e-07, 1.66526718e-07, 3.92258016e-07], [1.36270828e-07, 4.17841648e-07, 8.56405109e-07], [1.37541633e-07, 4.53750602e-07, 7.68512791e-07], [1.45518257e-07, 6.34355501e-07, 7.04753916e-07], [1.56007019e-07, 6.35820129e-07, 5.65314110e-07], [1.71676305e-07, 5.92261927e-07, 4.78777906e-07], [1.90442833e-07, 5.49946435e-07, 4.06831958e-07], [2.16033243e-07, 4.86720182e-07, 3.49111531e-07], [2.34895424e-07, 4.21473487e-07, 3.10066298e-07], [2.38446120e-07, 3.72554325e-07, 2.76426590e-07], [2.39987941e-07, 3.42585878e-07, 2.71347915e-07], [2.41708932e-07, 3.12257924e-07, 2.63682296e-07], [2.09086110e-07, 1.89499161e-07, 1.88648585e-07], [2.43611026e-07, 9.61760520e-08, 7.58583596e-08], [1.93665912e-07, 1.17051783e-07, 4.17802113e-08], [1.65643399e-07, 7.42967785e-08, 3.08687760e-08], [2.20749484e-07, 1.22858239e-07, 2.07973976e-08], [2.19819796e-07, 1.34363518e-07, 2.31354473e-08], [2.02686465e-07, 1.25603549e-07, 2.58839439e-08], [1.85131938e-07, 1.17201175e-07, 3.20020845e-08]]], dtype=float32) - chi(depth, hour, tau_bins)float323.153e-08 5.147e-08 ... 5.686e-09
- long_name :
- $χ$
- units :
- C^2/s
array([[[3.15311368e-08, 5.14684650e-08, 9.00696406e-08], [3.15410986e-08, 5.30324087e-08, 9.01792561e-08], [3.15075219e-08, 5.37205800e-08, 9.82160913e-08], [3.20624061e-08, 5.38295062e-08, 9.03460275e-08], [3.18296536e-08, 5.18737444e-08, 9.16030842e-08], [3.06467669e-08, 5.12408036e-08, 9.39065075e-08], [3.04877297e-08, 4.99954069e-08, 8.79619648e-08], [3.04382226e-08, 4.94467329e-08, 9.59539150e-08], [3.07606456e-08, 4.88297900e-08, 9.66301599e-08], [3.07989403e-08, 4.88755845e-08, 9.55644310e-08], [3.07602761e-08, 4.79528133e-08, 9.98426657e-08], [3.08518295e-08, 4.82226348e-08, 9.68349596e-08], [3.17893125e-08, 4.86282445e-08, 9.36632176e-08], [3.16449871e-08, 4.65035299e-08, 9.43617451e-08], [3.08822727e-08, 4.60562504e-08, 9.55800061e-08], [3.11754427e-08, 4.47915767e-08, 8.63022080e-08], [3.06896979e-08, 4.50557103e-08, 8.33114768e-08], [3.02294971e-08, 4.45752235e-08, 8.25163937e-08], [2.99401677e-08, 4.39887842e-08, 8.17240959e-08], [3.01301455e-08, 4.42664927e-08, 8.81232864e-08], ... [4.69678980e-08, 7.31207379e-08, 1.80065271e-07], [4.44437944e-08, 1.60526312e-07, 5.26232157e-07], [4.59130831e-08, 2.16911602e-07, 4.20312915e-07], [4.35025953e-08, 2.93830965e-07, 3.34603868e-07], [4.81800519e-08, 2.92471725e-07, 2.25865008e-07], [5.43300516e-08, 2.66074068e-07, 1.74853156e-07], [6.02832841e-08, 2.26358566e-07, 1.33450698e-07], [6.76159573e-08, 1.83526524e-07, 1.02248393e-07], [7.15624537e-08, 1.46292123e-07, 8.52350865e-08], [6.93415956e-08, 1.20850444e-07, 7.18475022e-08], [6.45878373e-08, 1.03103801e-07, 6.89958028e-08], [6.24963974e-08, 8.90549074e-08, 6.63747244e-08], [3.71018416e-08, 3.94645703e-08, 4.55000446e-08], [6.99339324e-08, 8.64094130e-09, 1.39391068e-08], [5.24933412e-08, 9.25000787e-09, 4.61419702e-09], [5.24419441e-08, 6.12304518e-09, 5.45677326e-09], [1.02914335e-07, 2.33580533e-08, 2.31011832e-09], [1.05326329e-07, 3.94027495e-08, 2.59428723e-09], [9.97600011e-08, 4.49156161e-08, 3.48193008e-09], [8.96089247e-08, 4.73158046e-08, 5.68561020e-09]]], dtype=float32) - Jb(depth, hour, tau_bins)float323.583e-10 4.566e-10 ... 4.18e-10
- cell_measures :
- area: areacello
- cell_methods :
- area:mean zi:point yh:mean xh:mean time: mean
- long_name :
- Total diapycnal diffusivity for heat at interfaces
- time_avg_info :
- average_T1,average_T2,average_DT
- units :
- m2 s-1
- standard_name :
- ocean_vertical_diffusive_buoyancy_flux
array([[[3.58277630e-10, 4.56598898e-10, 6.36808883e-10], [3.61272345e-10, 4.61960303e-10, 6.18004647e-10], [3.62497338e-10, 4.62224758e-10, 6.27580043e-10], [3.65806746e-10, 4.59576710e-10, 6.01129591e-10], [3.65918185e-10, 4.55617932e-10, 6.07656758e-10], [3.62261526e-10, 4.51856774e-10, 6.12711770e-10], [3.61771529e-10, 4.47667459e-10, 6.07582207e-10], [3.55180552e-10, 4.47039850e-10, 6.31304398e-10], [3.58695351e-10, 4.45621262e-10, 6.39133413e-10], [3.58532593e-10, 4.43890119e-10, 6.39311493e-10], [3.59802993e-10, 4.41411629e-10, 6.60119959e-10], [3.58949426e-10, 4.43239168e-10, 6.37624564e-10], [3.60242253e-10, 4.42435810e-10, 6.33906649e-10], [3.61484953e-10, 4.32591463e-10, 6.34564401e-10], [3.57910229e-10, 4.29199953e-10, 6.46178833e-10], [3.57660401e-10, 4.23346747e-10, 6.13737727e-10], [3.55238977e-10, 4.25607383e-10, 5.97280669e-10], [3.52947865e-10, 4.24758007e-10, 5.87040860e-10], [3.49784535e-10, 4.23031971e-10, 5.85980042e-10], [3.53015478e-10, 4.23206359e-10, 6.03543548e-10], ... [5.55786572e-09, 9.70253211e-09, 3.75396709e-08], [5.11393239e-09, 1.35741001e-08, 1.60814352e-07], [5.44395062e-09, 2.43575133e-08, 1.45002247e-07], [4.86894791e-09, 4.12963530e-08, 1.47366620e-07], [5.39292611e-09, 5.72370809e-08, 1.25824272e-07], [6.41620401e-09, 6.45678071e-08, 1.10190449e-07], [6.78160905e-09, 6.84167958e-08, 9.38289020e-08], [7.29576932e-09, 6.47739071e-08, 7.68193615e-08], [8.18336510e-09, 5.81149209e-08, 7.04859318e-08], [7.76166331e-09, 5.19686871e-08, 6.43213554e-08], [7.04196568e-09, 4.70384727e-08, 6.16404350e-08], [7.90036125e-09, 4.03301179e-08, 6.00140240e-08], [4.88817342e-09, 1.86461691e-08, 3.97448545e-08], [7.09699721e-09, 1.72511705e-09, 1.00553397e-08], [4.06853129e-09, 1.28853817e-09, 2.58423283e-09], [2.98437519e-09, 1.13034027e-09, 3.60601060e-09], [1.09980309e-08, 2.05348050e-09, 5.57511981e-10], [1.15927854e-08, 4.67873607e-09, 3.97649857e-10], [1.14538530e-08, 5.87054716e-09, 3.38541611e-10], [1.07085452e-08, 6.21498941e-09, 4.18006518e-10]]], dtype=float32) - Jq(depth, hour, tau_bins)float64-0.5587 -0.7393 ... -28.37 -5.81
- units :
- W/m^2
- long_name :
- $J_q^t$
array([[[ -0.55872717, -0.73933429, -1.01263507], [ -0.55909885, -0.74781458, -1.02032381], [ -0.56339972, -0.75235563, -1.04114909], [ -0.56574466, -0.75408049, -1.00271978], [ -0.56327974, -0.74221028, -0.9980942 ], [ -0.55951453, -0.73760955, -1.01441338], [ -0.55592129, -0.73105519, -0.98191639], [ -0.55362725, -0.72750384, -1.02130405], [ -0.56301865, -0.7252834 , -1.01789328], [ -0.55946494, -0.71693061, -1.03545581], [ -0.55886687, -0.71391623, -1.07142686], [ -0.55816062, -0.71663231, -1.04043361], [ -0.56094336, -0.71781622, -1.06883406], [ -0.55902317, -0.70272396, -1.05033251], [ -0.55030415, -0.69910558, -1.06303512], [ -0.55435328, -0.68816715, -0.99056348], [ -0.54662938, -0.68960124, -0.96040917], [ -0.5458248 , -0.68627143, -0.95080166], [ -0.54284984, -0.67667759, -0.94810626], [ -0.54519989, -0.67693639, -0.979253 ], ... [ -22.76061424, -33.70290482, -94.29752641], [ -20.68569739, -42.29942 , -335.72166172], [ -21.75314978, -72.79134959, -352.68323712], [ -20.33633891, -130.71658974, -353.91620765], [ -20.80126538, -172.30126404, -320.39020802], [ -22.98944112, -190.17481245, -293.028654 ], [ -25.53994451, -196.43233475, -267.77275736], [ -28.77743047, -187.48167067, -249.95806384], [ -32.94550126, -175.95360989, -226.59814176], [ -33.82984713, -164.83311868, -216.09387751], [ -33.77147363, -156.69712408, -208.27023105], [ -35.7552922 , -146.57650858, -204.76277986], [ -25.48603855, -75.301242 , -140.61098295], [ -33.06930083, -12.40822579, -42.20722391], [ -26.257377 , -10.45363752, -15.4468764 ], [ -24.46266471, -5.90238543, -13.88000668], [ -40.2315518 , -17.05878402, -4.50023447], [ -42.74567498, -27.01418861, -3.93155364], [ -40.26200269, -28.88526491, -3.4606702 ], [ -36.62022982, -28.36562722, -5.8097894 ]]]) - S2(depth, hour, tau_bins)float320.000118 0.0002866 ... 2.19e-05
- long_name :
- $S^2$
- units :
- s$^{-2}$
array([[[1.18035634e-04, 2.86617229e-04, 3.68310226e-04], [1.19055607e-04, 2.93288234e-04, 3.68738431e-04], [1.21223944e-04, 2.90943921e-04, 3.86649830e-04], [1.24606799e-04, 2.89873598e-04, 4.03484708e-04], [1.22047291e-04, 2.80318171e-04, 4.00653458e-04], [1.19750825e-04, 2.80132226e-04, 3.87836946e-04], [1.17275791e-04, 2.74971506e-04, 3.79573670e-04], [1.19338751e-04, 2.72942707e-04, 3.62854218e-04], [1.24937898e-04, 2.71977799e-04, 3.54683434e-04], [1.28315500e-04, 2.71855562e-04, 3.48416914e-04], [1.28547457e-04, 2.69131095e-04, 3.54306278e-04], [1.29285705e-04, 2.68015225e-04, 3.56286910e-04], [1.28866639e-04, 2.66737159e-04, 3.46874818e-04], [1.24962899e-04, 2.58250860e-04, 3.61833983e-04], [1.19088931e-04, 2.55677296e-04, 3.59144819e-04], [1.16096089e-04, 2.48583849e-04, 3.58901307e-04], [1.14474693e-04, 2.44785100e-04, 3.55322234e-04], [1.08996122e-04, 2.44438765e-04, 3.55026539e-04], [1.02533719e-04, 2.44985917e-04, 3.44370579e-04], [1.07392145e-04, 2.47505552e-04, 3.61505779e-04], ... [7.28670711e-05, 6.50857328e-05, 6.72916212e-05], [7.45321959e-05, 8.67422787e-05, 4.64802906e-05], [7.49734172e-05, 9.21883038e-05, 2.75155944e-05], [7.82151910e-05, 9.42244806e-05, 1.68252045e-05], [8.51124059e-05, 7.84320728e-05, 1.13622445e-05], [8.91593227e-05, 6.00015410e-05, 8.76838749e-06], [9.46019863e-05, 4.17266747e-05, 6.79051436e-06], [9.57649972e-05, 2.93372104e-05, 5.51613903e-06], [9.58165911e-05, 2.10807702e-05, 4.71648173e-06], [9.64381688e-05, 1.72020154e-05, 4.28609746e-06], [9.73792412e-05, 1.46298644e-05, 4.15710019e-06], [9.31051254e-05, 1.27367175e-05, 4.07165271e-06], [1.01596459e-04, 1.21337544e-05, 3.59626006e-06], [1.06722146e-04, 3.36617632e-05, 3.80803567e-06], [1.02554128e-04, 4.74796179e-05, 5.74421392e-06], [9.62065242e-05, 5.39669963e-05, 6.05823743e-06], [9.68023087e-05, 5.40903638e-05, 8.59525062e-06], [9.29079615e-05, 5.20505928e-05, 1.37420657e-05], [8.81354208e-05, 4.90190614e-05, 1.74025445e-05], [8.50617798e-05, 4.72261891e-05, 2.19031808e-05]]], dtype=float32) - N2(depth, hour, tau_bins)float320.0002707 0.0002679 ... 1.066e-05
- long_name :
- $N^2$
- units :
- s$^{-2}$
array([[[2.70653982e-04, 2.67854164e-04, 2.60556408e-04], [2.70489603e-04, 2.67931056e-04, 2.61637673e-04], [2.68508651e-04, 2.68075935e-04, 2.64729460e-04], [2.66491756e-04, 2.67974130e-04, 2.71308294e-04], [2.67325930e-04, 2.67938973e-04, 2.70344783e-04], [2.69618089e-04, 2.67519441e-04, 2.64699716e-04], [2.69445707e-04, 2.67475261e-04, 2.66230258e-04], [2.67186144e-04, 2.68850679e-04, 2.58856395e-04], [2.71684374e-04, 2.66605173e-04, 2.58436747e-04], [2.69634824e-04, 2.68864445e-04, 2.49675912e-04], [2.70803575e-04, 2.66544346e-04, 2.50487588e-04], [2.71231111e-04, 2.66110990e-04, 2.51161167e-04], [2.71857542e-04, 2.66223098e-04, 2.52874015e-04], [2.72617326e-04, 2.63849157e-04, 2.66338582e-04], [2.71545228e-04, 2.64220784e-04, 2.70796998e-04], [2.70907389e-04, 2.63898313e-04, 2.71208119e-04], [2.73490848e-04, 2.62059155e-04, 2.72291130e-04], [2.73298821e-04, 2.62513553e-04, 2.72275705e-04], [2.71756551e-04, 2.64893140e-04, 2.73101410e-04], [2.73922691e-04, 2.64201226e-04, 2.70985212e-04], ... [2.66783991e-05, 2.24435680e-05, 2.01482108e-05], [2.76282153e-05, 2.52834870e-05, 1.53130677e-05], [2.78609550e-05, 2.53739199e-05, 1.07697870e-05], [2.88161682e-05, 2.38241410e-05, 7.61099909e-06], [3.00592146e-05, 1.97585177e-05, 5.86610713e-06], [3.10059622e-05, 1.59887131e-05, 5.00141596e-06], [3.13461569e-05, 1.26825598e-05, 4.25584130e-06], [3.19165847e-05, 9.92587957e-06, 3.65537994e-06], [3.14851823e-05, 7.92554147e-06, 3.33652520e-06], [3.05563590e-05, 6.80374478e-06, 3.12953216e-06], [3.05634167e-05, 6.46581339e-06, 3.12648399e-06], [3.00894790e-05, 5.96192513e-06, 3.03067145e-06], [3.18050443e-05, 5.42599446e-06, 3.01429736e-06], [3.27169000e-05, 9.65514482e-06, 2.92601317e-06], [3.30679140e-05, 1.31469769e-05, 3.74685192e-06], [3.10162686e-05, 1.42103781e-05, 4.23103938e-06], [3.14569043e-05, 1.57434079e-05, 5.41504414e-06], [3.13854980e-05, 1.62794604e-05, 7.40211590e-06], [3.05814792e-05, 1.66734153e-05, 9.08484435e-06], [2.97653733e-05, 1.71468819e-05, 1.06635689e-05]]], dtype=float32) - Rig(depth, hour, tau_bins)float321.866 0.6854 ... 0.4224 0.5125
- long_name :
- $Ri^g$
array([[[1.8662848 , 0.68544173, 0.5350752 ], [1.8600645 , 0.6764416 , 0.52997637], [1.8082513 , 0.6684065 , 0.51994437], [1.7514868 , 0.66514564, 0.53821963], [1.7742116 , 0.6801465 , 0.5362674 ], [1.8050451 , 0.6883631 , 0.5284693 ], [1.826543 , 0.6967864 , 0.53718793], [1.839041 , 0.69765747, 0.538539 ], [1.8039743 , 0.69942325, 0.54343367], [1.762919 , 0.7034895 , 0.5462879 ], [1.7395374 , 0.71042335, 0.5473652 ], [1.743981 , 0.7068553 , 0.5498181 ], [1.7485907 , 0.7094732 , 0.5552983 ], [1.800962 , 0.7201221 , 0.56303406], [1.8770511 , 0.7300272 , 0.5768663 ], [1.8958896 , 0.7441837 , 0.59068596], [1.9319459 , 0.7465268 , 0.6032988 ], [1.9969598 , 0.76601106, 0.59619606], [2.1091611 , 0.77056324, 0.59924066], [2.072525 , 0.76344836, 0.5768527 ], ... [0.417879 , 0.3804391 , 0.3238179 ], [0.41090554, 0.30941188, 0.3488606 ], [0.403327 , 0.30385834, 0.392285 ], [0.39265534, 0.28617096, 0.46646762], [0.36620998, 0.2877478 , 0.5247332 ], [0.34303582, 0.31285378, 0.5649361 ], [0.3227075 , 0.34359443, 0.61189747], [0.3142567 , 0.38431424, 0.65183944], [0.30991292, 0.41828164, 0.6832897 ], [0.31734043, 0.44741976, 0.71992517], [0.32099736, 0.47542647, 0.747839 ], [0.33062443, 0.4994597 , 0.74357045], [0.32144618, 0.5118849 , 0.80607224], [0.31450492, 0.352032 , 0.76655614], [0.33332175, 0.34234598, 0.6389044 ], [0.35182685, 0.34710622, 0.6787852 ], [0.3533942 , 0.3665613 , 0.63400173], [0.36926734, 0.38762894, 0.5510011 ], [0.38271207, 0.4089583 , 0.52773184], [0.39180535, 0.42236108, 0.5125109 ]]], dtype=float32) - Rig_T(depth, hour, tau_bins)float321.75 0.6778 0.516 ... 0.2708 0.353
- long_name :
- $Ri^g_T$
array([[[1.7498139 , 0.6778394 , 0.5160031 ], [1.7240694 , 0.6695879 , 0.49940598], [1.6583761 , 0.6628067 , 0.498627 ], [1.6018262 , 0.658349 , 0.51457024], [1.6139219 , 0.66992456, 0.52054894], [1.6932245 , 0.6747377 , 0.5110692 ], [1.7320414 , 0.68135345, 0.5248196 ], [1.7112534 , 0.6885524 , 0.52049387], [1.6603553 , 0.69147515, 0.5276542 ], [1.6072863 , 0.6940439 , 0.542287 ], [1.5864432 , 0.70075834, 0.5364443 ], [1.586266 , 0.70577735, 0.54150844], [1.5927796 , 0.70689046, 0.5504924 ], [1.6602877 , 0.7092931 , 0.55996585], [1.7805405 , 0.7184469 , 0.5745784 ], [1.8147937 , 0.7288473 , 0.58771074], [1.8759367 , 0.7344651 , 0.59955335], [1.9077746 , 0.7484478 , 0.59346616], [1.9881756 , 0.7515857 , 0.5964693 ], [1.9673991 , 0.7486092 , 0.56754345], ... [0.2634116 , 0.2507721 , 0.24641225], [0.2607986 , 0.21899809, 0.26634485], [0.25649 , 0.2143133 , 0.2940078 ], [0.25049907, 0.20534892, 0.32926372], [0.23761362, 0.20744732, 0.35544658], [0.22342424, 0.21846932, 0.37724414], [0.21559256, 0.2321828 , 0.39727533], [0.21003583, 0.2461529 , 0.41424218], [0.21187592, 0.26617616, 0.4138385 ], [0.21543066, 0.27246305, 0.436479 ], [0.2142856 , 0.27827302, 0.4584359 ], [0.2159077 , 0.29173055, 0.43860143], [0.20547742, 0.29568854, 0.4857257 ], [0.21092497, 0.21182545, 0.4756171 ], [0.22476283, 0.2046566 , 0.37278977], [0.23309311, 0.20733707, 0.4022013 ], [0.23358554, 0.22945035, 0.39872727], [0.24027762, 0.24394931, 0.36718082], [0.24860656, 0.26064178, 0.36847693], [0.25379637, 0.27075374, 0.35295993]]], dtype=float32) - tau(hour, tau_bins)float320.0295 0.054 ... 0.05439 0.08311
- cell_methods :
- yh:mean xq:point time: mean
- interp_method :
- none
- long_name :
- Zonal surface stress from ocean interactions with atmos and ice
- time_avg_info :
- average_T1,average_T2,average_DT
- units :
- Pa
array([[0.0295027 , 0.05400424, 0.08233272], [0.029748 , 0.0539082 , 0.08225691], [0.02973902, 0.05381709, 0.08261511], [0.02958953, 0.05378674, 0.08290136], [0.02952781, 0.0541986 , 0.08320552], [0.02957567, 0.05422445, 0.08379569], [0.02954055, 0.05467816, 0.08413152], [0.02949499, 0.05448272, 0.08375749], [0.02946378, 0.05434905, 0.08394575], [0.02955062, 0.05434605, 0.08510967], [0.02943835, 0.05417863, 0.08425807], [0.02927817, 0.05410217, 0.08441167], [0.02894278, 0.05420254, 0.08438533], [0.02929234, 0.05503666, 0.08478092], [0.0293923 , 0.05574505, 0.08499894], [0.0296241 , 0.05632416, 0.08501871], [0.03005047, 0.05656263, 0.08522117], [0.03020069, 0.05679295, 0.08566844], [0.03010707, 0.05716396, 0.08595885], [0.02992886, 0.05671743, 0.08496676], [0.03006576, 0.05620772, 0.08470601], [0.02999885, 0.05570246, 0.08516616], [0.02975408, 0.05507209, 0.08390015], [0.02965912, 0.05439087, 0.0831105 ]], dtype=float32)
- title :
- baseline
<xarray.DatasetView> Dimensions: (depth: 6, hour: 24, tau_bins: 3) Coordinates: * depth (depth) float64 -89.0 -69.0 -59.0 -49.0 -39.0 -29.0 xh float64 -140.0 yh float64 0.0625 yq float64 -0.0625 * hour (hour) int64 0 1 2 3 4 5 6 7 8 9 ... 14 15 16 17 18 19 20 21 22 23 * tau_bins (tau_bins) object (0.0, 0.04] (0.04, 0.075] (0.075, inf] Data variables: KT (depth, hour, tau_bins) float32 1.001e-06 1.001e-06 ... 0.0006551 eps (depth, hour, tau_bins) float32 3.001e-08 1.736e-07 ... 3.2e-08 chi (depth, hour, tau_bins) float32 3.153e-08 5.147e-08 ... 5.686e-09 Jb (depth, hour, tau_bins) float32 3.583e-10 4.566e-10 ... 4.18e-10 Jq (depth, hour, tau_bins) float64 -0.5587 -0.7393 ... -28.37 -5.81 S2 (depth, hour, tau_bins) float32 0.000118 0.0002866 ... 2.19e-05 N2 (depth, hour, tau_bins) float32 0.0002707 0.0002679 ... 1.066e-05 Rig (depth, hour, tau_bins) float32 1.866 0.6854 ... 0.4224 0.5125 Rig_T (depth, hour, tau_bins) float32 1.75 0.6778 0.516 ... 0.2708 0.353 tau (hour, tau_bins) float32 0.0295 0.054 0.08233 ... 0.05439 0.08311 Attributes: title: baselinebaseline.001- depth: 6
- hour: 24
- tau_bins: 3
- depth(depth)float64-89.0 -69.0 -59.0 -49.0 -39.0 -29.0
- cartesian_axis :
- Z
- long_name :
- Interface pseudo-depth, -z*
- positive :
- up
- units :
- meter
array([-89., -69., -59., -49., -39., -29.])
- xh()float64-140.0
- cartesian_axis :
- X
- domain_decomposition :
- [220, 222, 220, 221]
- long_name :
- h point nominal longitude
- units :
- degrees_east
array(-140.)
- yh()float640.0625
- cartesian_axis :
- Y
- domain_decomposition :
- [210, 258, 210, 221]
- long_name :
- h point nominal latitude
- units :
- degrees_north
array(0.06249997)
- yq()float64-0.0625
- cartesian_axis :
- Y
- domain_decomposition :
- [209, 257, 209, 221]
- long_name :
- q point nominal latitude
- units :
- degrees_north
array(-0.06249997)
- hour(hour)int640 1 2 3 4 5 6 ... 18 19 20 21 22 23
array([ 0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23]) - tau_bins(tau_bins)object(0.0, 0.04] ... (0.075, inf]
- cell_methods :
- yh:mean xq:point time: mean
- interp_method :
- none
- long_name :
- Zonal surface stress from ocean interactions with atmos and ice
- time_avg_info :
- average_T1,average_T2,average_DT
- units :
- Pa
array([Interval(0.0, 0.04, closed='right'), Interval(0.04, 0.075, closed='right'), Interval(0.075, inf, closed='right')], dtype=object)
- KT(depth, hour, tau_bins)float321.001e-06 1.001e-06 ... 0.0006196
- cell_measures :
- area: areacello
- cell_methods :
- area:mean zi:point yh:mean xh:mean time: mean
- long_name :
- Total diapycnal diffusivity for heat at interfaces
- standard_name :
- ocean_vertical_heat_diffusivity
- time_avg_info :
- average_T1,average_T2,average_DT
- units :
- m2 s-1
array([[[1.00062641e-06, 1.00062653e-06, 1.00062653e-06], [1.00062641e-06, 1.00062664e-06, 1.00062653e-06], [1.00062641e-06, 1.00062653e-06, 1.00062653e-06], [1.00062641e-06, 1.00062653e-06, 1.00062664e-06], [1.00062641e-06, 1.00062653e-06, 1.00062653e-06], [1.00062641e-06, 1.00062653e-06, 1.00062653e-06], [1.00062641e-06, 1.00062653e-06, 1.00062653e-06], [1.00062641e-06, 1.00062653e-06, 1.00062653e-06], [1.00062641e-06, 1.00062653e-06, 1.00062653e-06], [1.00062641e-06, 1.00062653e-06, 1.00062664e-06], [1.00062641e-06, 1.00062653e-06, 1.00062664e-06], [1.00062641e-06, 1.00062653e-06, 1.00062653e-06], [1.00062641e-06, 1.00062653e-06, 1.00062664e-06], [1.00062641e-06, 1.00062653e-06, 1.00062664e-06], [1.00062641e-06, 1.00062653e-06, 1.00062664e-06], [1.00062641e-06, 1.00062653e-06, 1.00062653e-06], [1.00062641e-06, 1.00062653e-06, 1.00062653e-06], [1.00062641e-06, 1.00062653e-06, 1.00062653e-06], [1.00062641e-06, 1.00062653e-06, 1.00062653e-06], [1.00062641e-06, 1.00062653e-06, 1.00062653e-06], ... [8.69949930e-04, 9.59244964e-04, 1.11343141e-03], [8.50179931e-04, 1.02828699e-03, 1.24498047e-02], [8.22628965e-04, 1.05729094e-03, 3.77871096e-03], [8.18862813e-04, 1.26605667e-03, 2.33795643e-02], [8.30616336e-04, 1.36252248e-03, 3.00153233e-02], [8.26583593e-04, 1.66581431e-03, 3.50915715e-02], [8.35646526e-04, 2.89651891e-03, 4.03600074e-02], [8.40844936e-04, 5.73793054e-03, 4.29606661e-02], [8.83748115e-04, 8.86736996e-03, 4.64188121e-02], [8.89282324e-04, 1.00382138e-02, 4.53674309e-02], [9.06878675e-04, 1.16374400e-02, 4.41440344e-02], [9.52538336e-04, 1.18752746e-02, 4.22920287e-02], [9.77167627e-04, 3.11270822e-03, 2.84510609e-02], [1.07384217e-03, 1.43216818e-03, 3.58461030e-03], [9.83901555e-04, 1.30531006e-03, 1.26952492e-03], [9.94352158e-04, 8.17729277e-04, 1.27175078e-03], [1.12384127e-03, 1.34078134e-03, 5.45061426e-04], [1.12999673e-03, 1.29678287e-03, 6.57544471e-04], [1.10153994e-03, 1.23138377e-03, 6.13115670e-04], [1.06722605e-03, 1.15680229e-03, 6.19593775e-04]]], dtype=float32) - eps(depth, hour, tau_bins)float323.571e-09 7.384e-09 ... 4.795e-08
- long_name :
- $ε$
- units :
- W/kg
array([[[3.57124752e-09, 7.38426387e-09, 1.14503083e-08], [3.46380302e-09, 7.70825714e-09, 1.21308972e-08], [3.55494123e-09, 7.66528174e-09, 1.25291590e-08], [3.64194630e-09, 7.73805198e-09, 1.09109886e-08], [3.66173114e-09, 7.54748086e-09, 1.32884317e-08], [3.56473562e-09, 7.45457740e-09, 1.38303182e-08], [3.53924134e-09, 7.21436511e-09, 1.41857326e-08], [3.55269147e-09, 7.17422743e-09, 1.52570259e-08], [3.60691987e-09, 7.19619075e-09, 1.58858029e-08], [3.62712704e-09, 6.97547353e-09, 1.70368057e-08], [3.57896757e-09, 7.23481275e-09, 1.65586016e-08], [3.63070085e-09, 7.11863546e-09, 1.52314037e-08], [3.65208352e-09, 7.28103622e-09, 1.54543844e-08], [3.59472363e-09, 7.15794535e-09, 1.41217127e-08], [3.48057760e-09, 7.03329484e-09, 1.39779228e-08], [3.43757622e-09, 6.78592382e-09, 1.22623325e-08], [3.30933392e-09, 6.82196699e-09, 1.17316370e-08], [3.28501937e-09, 6.64459865e-09, 1.14167511e-08], [3.28050898e-09, 6.34667874e-09, 1.17305454e-08], [3.30512262e-09, 6.53624355e-09, 1.20629728e-08], ... [1.97078904e-07, 1.75519631e-07, 2.64559191e-07], [1.91761814e-07, 2.66213419e-07, 1.40613520e-06], [1.88211999e-07, 2.84233721e-07, 8.98357257e-07], [1.88038371e-07, 4.99263649e-07, 1.16582873e-06], [1.96016416e-07, 7.13196641e-07, 1.02670094e-06], [2.06843083e-07, 8.00393195e-07, 9.15784995e-07], [2.20204754e-07, 8.53189874e-07, 7.59640102e-07], [2.31497069e-07, 8.06180196e-07, 6.55824294e-07], [2.52628126e-07, 7.68301788e-07, 5.73569423e-07], [2.59949047e-07, 7.08147411e-07, 5.34952221e-07], [2.69296123e-07, 6.53131451e-07, 4.97154474e-07], [2.78680091e-07, 6.14048531e-07, 4.91469621e-07], [2.62996366e-07, 4.09287452e-07, 3.08206154e-07], [2.75822629e-07, 2.59727585e-07, 1.09740199e-07], [2.34932443e-07, 2.82902306e-07, 7.34295185e-08], [2.35040972e-07, 2.03696345e-07, 5.82381858e-08], [2.72802367e-07, 2.68030021e-07, 3.45247777e-08], [2.74928141e-07, 2.46962657e-07, 4.32012790e-08], [2.64318601e-07, 2.23850662e-07, 4.75941953e-08], [2.49914194e-07, 2.03934263e-07, 4.79486744e-08]]], dtype=float32) - chi(depth, hour, tau_bins)float321.417e-08 1.12e-08 ... 1.021e-08
- long_name :
- $χ$
- units :
- C^2/s
array([[[1.41654599e-08, 1.12043317e-08, 1.14997345e-08], [1.43564414e-08, 1.10966809e-08, 1.06867253e-08], [1.40540273e-08, 1.12314247e-08, 1.03123279e-08], [1.41355176e-08, 1.13349161e-08, 1.00236583e-08], [1.42605980e-08, 1.10975300e-08, 1.07465459e-08], [1.42887382e-08, 1.11918066e-08, 1.07850138e-08], [1.43708263e-08, 1.12686127e-08, 1.08477582e-08], [1.42204337e-08, 1.12907834e-08, 1.09332676e-08], [1.43949102e-08, 1.13406422e-08, 1.08743325e-08], [1.42460177e-08, 1.14293757e-08, 1.04407292e-08], [1.40131853e-08, 1.15438645e-08, 1.04089919e-08], [1.38146916e-08, 1.15858061e-08, 1.04847828e-08], [1.37048985e-08, 1.15371819e-08, 1.06222746e-08], [1.38580338e-08, 1.15255503e-08, 1.07217994e-08], [1.39460585e-08, 1.15467724e-08, 1.07971161e-08], [1.40707135e-08, 1.14792176e-08, 1.11246070e-08], [1.44085774e-08, 1.13738761e-08, 1.11539817e-08], [1.43768464e-08, 1.14945884e-08, 1.12381482e-08], [1.44178420e-08, 1.15233316e-08, 1.11600116e-08], [1.44640957e-08, 1.12931424e-08, 1.13877565e-08], ... [1.43766911e-07, 1.03006442e-07, 8.78815598e-08], [1.37191023e-07, 1.83328794e-07, 7.09568042e-07], [1.25446263e-07, 1.67309494e-07, 3.59490400e-07], [1.29034063e-07, 2.71363888e-07, 5.55303927e-07], [1.28467946e-07, 3.13767430e-07, 4.10859371e-07], [1.31779615e-07, 3.50028699e-07, 3.47528783e-07], [1.37602086e-07, 3.64599771e-07, 2.63568069e-07], [1.47534877e-07, 3.30650295e-07, 2.13382762e-07], [1.61122401e-07, 2.85556155e-07, 1.73867647e-07], [1.52407182e-07, 2.44686731e-07, 1.56882791e-07], [1.51709003e-07, 2.20665001e-07, 1.45941712e-07], [1.50763512e-07, 1.98459645e-07, 1.46552651e-07], [1.29097174e-07, 8.24987865e-08, 7.70501956e-08], [1.57343763e-07, 4.65327865e-08, 1.47697543e-08], [1.21017209e-07, 3.88323684e-08, 6.95555213e-09], [1.44461794e-07, 2.23305978e-08, 6.47459286e-09], [1.90294784e-07, 1.18683403e-07, 3.80536269e-09], [2.08375866e-07, 1.24235868e-07, 7.61631114e-09], [2.02433256e-07, 1.21694356e-07, 1.00547233e-08], [1.91222796e-07, 1.17619521e-07, 1.02139150e-08]]], dtype=float32) - Jb(depth, hour, tau_bins)float322.363e-10 2.037e-10 ... 8.176e-10
- cell_measures :
- area: areacello
- cell_methods :
- area:mean zi:point yh:mean xh:mean time: mean
- long_name :
- Total diapycnal diffusivity for heat at interfaces
- standard_name :
- ocean_vertical_diffusive_buoyancy_flux
- time_avg_info :
- average_T1,average_T2,average_DT
- units :
- m2 s-1
array([[[2.36305003e-10, 2.03665917e-10, 2.08056850e-10], [2.36786424e-10, 2.02221850e-10, 2.09279122e-10], [2.33130792e-10, 2.03391748e-10, 2.06526990e-10], [2.32916963e-10, 2.05656311e-10, 2.00404207e-10], [2.33894487e-10, 2.05576237e-10, 2.02458952e-10], [2.35067965e-10, 2.06578477e-10, 2.01200598e-10], [2.36723557e-10, 2.07841605e-10, 1.97894201e-10], [2.34675418e-10, 2.07456149e-10, 2.04126174e-10], [2.35204634e-10, 2.06371822e-10, 2.01659162e-10], [2.33386255e-10, 2.09510145e-10, 1.95423525e-10], [2.32901309e-10, 2.10196471e-10, 1.95556377e-10], [2.29793545e-10, 2.10664777e-10, 1.96119843e-10], [2.28470382e-10, 2.09507939e-10, 1.99778888e-10], [2.31024630e-10, 2.09797971e-10, 1.99606068e-10], [2.35091085e-10, 2.09259596e-10, 1.99032207e-10], [2.39884834e-10, 2.09699591e-10, 2.02228262e-10], [2.45257092e-10, 2.07174472e-10, 2.03287082e-10], [2.45073906e-10, 2.07818360e-10, 2.04217240e-10], [2.46258847e-10, 2.07750095e-10, 2.05482159e-10], [2.46289100e-10, 2.06136733e-10, 2.08115400e-10], ... [1.10021112e-08, 1.07078728e-08, 9.40200451e-09], [1.06592415e-08, 1.59663145e-08, 1.32835780e-07], [1.03445750e-08, 1.54878776e-08, 5.95220513e-08], [1.09041141e-08, 2.38682460e-08, 1.59961715e-07], [1.09902807e-08, 3.03844914e-08, 1.49897033e-07], [1.09048992e-08, 3.81727965e-08, 1.61086376e-07], [1.12891199e-08, 5.17055732e-08, 1.47693413e-07], [1.13750245e-08, 5.63804861e-08, 1.32643848e-07], [1.32148603e-08, 6.07146688e-08, 1.20656594e-07], [1.23768604e-08, 5.72766012e-08, 1.13463010e-07], [1.22800037e-08, 5.40147020e-08, 1.14951519e-07], [1.20845041e-08, 4.98633597e-08, 1.05718762e-07], [9.85280746e-09, 2.33101858e-08, 5.91517733e-08], [1.16605072e-08, 4.99037567e-09, 8.04534306e-09], [7.44766782e-09, 3.38226780e-09, 2.08077888e-09], [8.68693206e-09, 1.48512147e-09, 3.18790239e-09], [1.40054999e-08, 9.78285009e-09, 3.67206598e-10], [1.44412642e-08, 1.08139648e-08, 5.36393763e-10], [1.42424401e-08, 1.11552581e-08, 6.93334057e-10], [1.40376359e-08, 1.11661604e-08, 8.17562462e-10]]], dtype=float32) - Jq(depth, hour, tau_bins)float64-0.3639 -0.3232 ... -34.57 -7.167
- units :
- W/m^2
- long_name :
- $J_q^t$
array([[[-3.63856336e-01, -3.23210307e-01, -3.25944868e-01], [-3.64410742e-01, -3.22438626e-01, -3.14459372e-01], [-3.60407654e-01, -3.23780796e-01, -3.09333925e-01], [-3.60840765e-01, -3.25704081e-01, -3.03560250e-01], [-3.63860861e-01, -3.23031085e-01, -3.12243092e-01], [-3.65510015e-01, -3.23045562e-01, -3.13675629e-01], [-3.65869896e-01, -3.24214106e-01, -3.16109657e-01], [-3.64828718e-01, -3.25053940e-01, -3.17998895e-01], [-3.66923519e-01, -3.24808720e-01, -3.16305131e-01], [-3.64535768e-01, -3.27514952e-01, -3.11441584e-01], [-3.62548931e-01, -3.28812389e-01, -3.11511710e-01], [-3.59663507e-01, -3.29657957e-01, -3.14064940e-01], [-3.57138041e-01, -3.28465274e-01, -3.13897933e-01], [-3.58997834e-01, -3.27921523e-01, -3.16069328e-01], [-3.61749182e-01, -3.27854408e-01, -3.15479682e-01], [-3.61282345e-01, -3.26603508e-01, -3.20980527e-01], [-3.66450614e-01, -3.26124243e-01, -3.21068923e-01], [-3.64773864e-01, -3.27023533e-01, -3.22861464e-01], [-3.65693197e-01, -3.27779466e-01, -3.23091044e-01], [-3.66243552e-01, -3.25203886e-01, -3.25132215e-01], ... [-3.11515463e+01, -2.95920814e+01, -3.13240759e+01], [-2.97588069e+01, -4.01929936e+01, -3.26898750e+02], [-2.80524053e+01, -3.79606483e+01, -1.52565274e+02], [-2.87241693e+01, -5.96539726e+01, -3.31737156e+02], [-2.92523879e+01, -7.74188050e+01, -3.17598502e+02], [-2.97933349e+01, -1.03626681e+02, -3.14021997e+02], [-3.06915511e+01, -1.33709094e+02, -2.94227293e+02], [-3.16197143e+01, -1.50164779e+02, -2.75888518e+02], [-3.48487573e+01, -1.54185040e+02, -2.59916040e+02], [-3.53652229e+01, -1.47535313e+02, -2.41424957e+02], [-3.63852525e+01, -1.43487593e+02, -2.32847570e+02], [-3.87944462e+01, -1.38562537e+02, -2.20087752e+02], [-3.47095415e+01, -6.84364904e+01, -1.35048197e+02], [-3.79342051e+01, -2.57598461e+01, -2.68607238e+01], [-3.00724935e+01, -1.82040015e+01, -1.15180749e+01], [-3.35407877e+01, -9.14485579e+00, -9.79402621e+00], [-4.07352508e+01, -3.76753676e+01, -3.51846569e+00], [-4.24517449e+01, -3.83129382e+01, -5.99288931e+00], [-4.14190661e+01, -3.68610034e+01, -7.49174915e+00], [-3.99809860e+01, -3.45665587e+01, -7.16703748e+00]]]) - S2(depth, hour, tau_bins)float321.608e-05 3.475e-05 ... 4.999e-05
- long_name :
- $S^2$
- units :
- s$^{-2}$
array([[[1.60773343e-05, 3.47473433e-05, 5.45502517e-05], [1.55510897e-05, 3.62657956e-05, 5.80403103e-05], [1.57532741e-05, 3.61047132e-05, 5.88809962e-05], [1.61362404e-05, 3.65621709e-05, 5.16177024e-05], [1.63347595e-05, 3.55188567e-05, 6.21003710e-05], [1.59758565e-05, 3.49079783e-05, 6.53139577e-05], [1.57272680e-05, 3.38322498e-05, 6.75400515e-05], [1.60485488e-05, 3.36952726e-05, 7.21537217e-05], [1.64259400e-05, 3.36227749e-05, 6.97661089e-05], [1.64107878e-05, 3.27744456e-05, 8.03719886e-05], [1.60798663e-05, 3.40344159e-05, 7.83935102e-05], [1.63162404e-05, 3.36289268e-05, 7.09879023e-05], [1.63955192e-05, 3.41706000e-05, 7.23957492e-05], [1.62350207e-05, 3.38442551e-05, 6.78550277e-05], [1.52499942e-05, 3.28904680e-05, 6.58997596e-05], [1.51459171e-05, 3.21342086e-05, 5.73855250e-05], [1.46635284e-05, 3.17903687e-05, 5.46010815e-05], [1.46638777e-05, 3.11573203e-05, 5.30258621e-05], [1.46625471e-05, 2.97258357e-05, 5.51550838e-05], [1.46957009e-05, 3.03876823e-05, 5.71436394e-05], ... [1.76954345e-04, 1.31229055e-04, 1.19671509e-04], [1.75386085e-04, 1.44285470e-04, 9.24286651e-05], [1.76493719e-04, 1.65989506e-04, 9.93364883e-05], [1.77259906e-04, 1.83417767e-04, 6.67022250e-05], [1.80237097e-04, 2.02479947e-04, 4.05998107e-05], [1.86967096e-04, 2.01381699e-04, 2.87006660e-05], [1.91430430e-04, 1.85397657e-04, 2.00318373e-05], [1.97789792e-04, 1.57587536e-04, 1.56843635e-05], [2.03315052e-04, 1.19843135e-04, 1.30644439e-05], [2.03890755e-04, 9.93930225e-05, 1.25701235e-05], [2.09443897e-04, 8.31915386e-05, 1.15447201e-05], [2.06869125e-04, 7.98302353e-05, 1.13064698e-05], [2.16365268e-04, 1.05084415e-04, 9.60457783e-06], [2.22570961e-04, 1.54111607e-04, 1.23858135e-05], [2.14604661e-04, 1.80870440e-04, 1.95051653e-05], [2.11807986e-04, 1.81155774e-04, 2.01697949e-05], [2.13664738e-04, 1.67245424e-04, 2.97912757e-05], [2.12474784e-04, 1.55046437e-04, 3.86240499e-05], [2.08408193e-04, 1.45126833e-04, 4.46427548e-05], [2.01206974e-04, 1.38201241e-04, 4.99901362e-05]]], dtype=float32) - N2(depth, hour, tau_bins)float320.0002237 0.0002014 ... 1.187e-05
- cell_measures :
- area: areacello
- cell_methods :
- area:mean zi:point yh:mean xh:mean time: mean
- long_name :
- Buoyancy frequency squared
- time_avg_info :
- average_T1,average_T2,average_DT
- units :
- s-2
array([[[2.23716459e-04, 2.01369432e-04, 2.07364807e-04], [2.24670584e-04, 2.00559298e-04, 2.04587486e-04], [2.22044764e-04, 2.02383279e-04, 2.01458635e-04], [2.21417606e-04, 2.03395641e-04, 1.95091299e-04], [2.21755530e-04, 2.02223877e-04, 2.01670875e-04], [2.21897033e-04, 2.02601805e-04, 2.01380739e-04], [2.21933631e-04, 2.02978175e-04, 2.01497838e-04], [2.20619098e-04, 2.03436794e-04, 2.02596842e-04], [2.21749637e-04, 2.02988711e-04, 2.01417221e-04], [2.19857873e-04, 2.04759723e-04, 1.99296846e-04], [2.19346490e-04, 2.05749850e-04, 1.98707756e-04], [2.18359230e-04, 2.06736469e-04, 2.01719973e-04], [2.18257366e-04, 2.06634082e-04, 1.97994610e-04], [2.19672744e-04, 2.05267948e-04, 2.00647381e-04], [2.20484260e-04, 2.05869466e-04, 1.96984562e-04], [2.22277275e-04, 2.05256205e-04, 2.00871931e-04], [2.22674425e-04, 2.04421536e-04, 2.00914845e-04], [2.22843708e-04, 2.05170858e-04, 2.03752279e-04], [2.24134274e-04, 2.05697404e-04, 2.03576696e-04], [2.23726267e-04, 2.04792828e-04, 2.06141849e-04], ... [4.44578545e-05, 3.09555980e-05, 2.18656496e-05], [4.37544404e-05, 3.26075969e-05, 2.38331431e-05], [4.39932483e-05, 3.30363655e-05, 2.08300789e-05], [4.41018747e-05, 3.41599880e-05, 1.92035659e-05], [4.40224831e-05, 3.48551039e-05, 1.46976718e-05], [4.41208649e-05, 3.49741385e-05, 1.14134200e-05], [4.50448970e-05, 3.32406053e-05, 8.23586197e-06], [4.56341149e-05, 2.86893446e-05, 7.04849163e-06], [4.63023462e-05, 2.27672244e-05, 6.17078695e-06], [4.63085671e-05, 2.06949808e-05, 5.77945048e-06], [4.70361374e-05, 1.78653881e-05, 5.42836233e-06], [4.65924677e-05, 1.72670952e-05, 5.22179744e-06], [4.66712881e-05, 1.52227567e-05, 4.89958938e-06], [4.81022325e-05, 1.52632801e-05, 4.34061121e-06], [4.82442083e-05, 2.59471744e-05, 4.41810153e-06], [4.77407229e-05, 2.84563885e-05, 5.11112830e-06], [4.76316200e-05, 2.91304204e-05, 5.96908694e-06], [4.79773953e-05, 2.88553783e-05, 8.46229523e-06], [4.80109811e-05, 2.87334278e-05, 1.05174622e-05], [4.71629573e-05, 2.89586733e-05, 1.18731123e-05]]], dtype=float32) - Rig(depth, hour, tau_bins)float3213.2 4.297 3.056 ... 0.2232 0.2512
- cell_measures :
- area: areacello
- cell_methods :
- area:mean zi:point yh:mean xh:mean time: mean
- long_name :
- $Ri^g$
- time_avg_info :
- average_T1,average_T2,average_DT
array([[[13.197857 , 4.2973194 , 3.0555031 ], [13.821407 , 4.232544 , 2.5504222 ], [13.510264 , 4.2386236 , 2.4945803 ], [13.20141 , 4.2471313 , 2.4653025 ], [13.149447 , 4.319394 , 2.4220955 ], [13.85933 , 4.4229817 , 2.3923457 ], [14.107593 , 4.5487447 , 2.3519185 ], [13.647481 , 4.598955 , 2.3381877 ], [13.462015 , 4.5880175 , 2.3576286 ], [13.113719 , 4.6214905 , 2.182707 ], [13.353025 , 4.4489136 , 2.2182493 ], [13.087497 , 4.5611644 , 2.2721744 ], [12.944699 , 4.5314035 , 2.3206887 ], [12.845064 , 4.613967 , 2.453578 ], [13.476563 , 4.813492 , 2.4708343 ], [13.658272 , 5.07368 , 2.7000542 ], [14.541157 , 5.212734 , 2.7889817 ], [14.787399 , 5.2871675 , 2.8942657 ], [15.181756 , 5.5781765 , 2.8768818 ], [15.022156 , 5.3412776 , 2.904497 ], ... [ 0.2605168 , 0.24626791, 0.2186516 ], [ 0.26234898, 0.24100047, 0.27151155], [ 0.26460433, 0.22231369, 0.22628295], [ 0.26444855, 0.21137132, 0.32527298], [ 0.26379338, 0.2065137 , 0.37389553], [ 0.26258442, 0.20844041, 0.3997121 ], [ 0.25847906, 0.22196066, 0.4238111 ], [ 0.25546002, 0.2310276 , 0.44557902], [ 0.24929616, 0.24503568, 0.47095898], [ 0.25088966, 0.25116956, 0.47218096], [ 0.25009263, 0.25732547, 0.46698648], [ 0.24756661, 0.26599523, 0.46304277], [ 0.22898337, 0.22704817, 0.5012692 ], [ 0.21460304, 0.16282685, 0.3467214 ], [ 0.22194551, 0.16865185, 0.23832142], [ 0.223914 , 0.17884398, 0.23131603], [ 0.22466302, 0.19137523, 0.21537249], [ 0.22704174, 0.20382346, 0.23264292], [ 0.23163387, 0.21370158, 0.24267618], [ 0.2359288 , 0.22320737, 0.25116214]]], dtype=float32) - Rig_T(depth, hour, tau_bins)float3213.39 4.173 3.016 ... 0.1836 0.2279
- long_name :
- $Ri^g_T$
array([[[13.39003 , 4.1733675 , 3.0161352 ], [14.119032 , 4.050316 , 2.7403107 ], [13.708002 , 4.044218 , 2.772739 ], [13.35049 , 4.09664 , 2.7337017 ], [13.440435 , 4.2385445 , 2.5692105 ], [13.888355 , 4.367624 , 2.4463243 ], [14.08926 , 4.548192 , 2.2969518 ], [13.814713 , 4.5322876 , 2.2213016 ], [13.61069 , 4.5070376 , 2.2090302 ], [13.551187 , 4.5452027 , 2.0633187 ], [13.787437 , 4.4257965 , 2.0763931 ], [13.39185 , 4.480337 , 2.1504233 ], [13.147581 , 4.426073 , 2.1903646 ], [13.178958 , 4.545905 , 2.2907367 ], [13.68602 , 4.709573 , 2.301371 ], [13.814043 , 4.880429 , 2.4320097 ], [14.562265 , 5.046787 , 2.570214 ], [14.989765 , 5.1713166 , 2.7265682 ], [15.491265 , 5.3896675 , 2.7282755 ], [15.379088 , 5.16234 , 2.7647042 ], ... [ 0.19949941, 0.19074827, 0.17702514], [ 0.20222469, 0.18197736, 0.18649122], [ 0.20144732, 0.16863236, 0.17766082], [ 0.20056407, 0.15703726, 0.18814558], [ 0.20053883, 0.14603414, 0.20615524], [ 0.1938774 , 0.14094096, 0.23102301], [ 0.18646285, 0.14410391, 0.25327593], [ 0.1793328 , 0.14658612, 0.28199226], [ 0.16956541, 0.1529384 , 0.29382858], [ 0.16857266, 0.15509175, 0.3094122 ], [ 0.16314791, 0.15785547, 0.30625468], [ 0.16122043, 0.16049628, 0.3079362 ], [ 0.15586448, 0.14820942, 0.3269163 ], [ 0.15939882, 0.13319623, 0.2494062 ], [ 0.16583118, 0.13827197, 0.19804096], [ 0.1705277 , 0.1412782 , 0.19478822], [ 0.17406169, 0.15591256, 0.21430875], [ 0.17862558, 0.16688593, 0.21968636], [ 0.1818856 , 0.17532237, 0.22169115], [ 0.18481219, 0.18359019, 0.22792467]]], dtype=float32) - tau(hour, tau_bins)float320.02931 0.05352 ... 0.0542 0.08266
- cell_methods :
- yh:mean xq:point time: mean
- interp_method :
- none
- long_name :
- Zonal surface stress from ocean interactions with atmos and ice
- time_avg_info :
- average_T1,average_T2,average_DT
- units :
- Pa
array([[0.02931377, 0.05352184, 0.08240295], [0.02966809, 0.05379798, 0.08218867], [0.02963378, 0.0531844 , 0.08235388], [0.02950549, 0.05342022, 0.08332323], [0.02959662, 0.05367155, 0.08316955], [0.0294683 , 0.05404468, 0.08309559], [0.02944375, 0.05421706, 0.08337168], [0.02952339, 0.05432522, 0.08384206], [0.02939557, 0.05427076, 0.08357246], [0.02919702, 0.05400485, 0.08424108], [0.02917114, 0.05389731, 0.08418184], [0.02895542, 0.05386308, 0.08414546], [0.02882244, 0.05380915, 0.0840569 ], [0.02902017, 0.05480089, 0.08480587], [0.02929266, 0.05536883, 0.08480069], [0.02949213, 0.05573643, 0.08509558], [0.02968458, 0.05615301, 0.08533888], [0.02993085, 0.05675283, 0.08584618], [0.02995646, 0.05718726, 0.08638418], [0.02968866, 0.05656635, 0.08475825], [0.0296479 , 0.05585978, 0.08439424], [0.02981201, 0.05550362, 0.08500274], [0.02963592, 0.0546338 , 0.08361351], [0.02963188, 0.05420088, 0.08266187]], dtype=float32)
- title :
- KPP ν0=2.5, Ric=0.2, Ri0=0.5
<xarray.DatasetView> Dimensions: (depth: 6, hour: 24, tau_bins: 3) Coordinates: * depth (depth) float64 -89.0 -69.0 -59.0 -49.0 -39.0 -29.0 xh float64 -140.0 yh float64 0.0625 yq float64 -0.0625 * hour (hour) int64 0 1 2 3 4 5 6 7 8 9 ... 14 15 16 17 18 19 20 21 22 23 * tau_bins (tau_bins) object (0.0, 0.04] (0.04, 0.075] (0.075, inf] Data variables: KT (depth, hour, tau_bins) float32 1.001e-06 1.001e-06 ... 0.0006196 eps (depth, hour, tau_bins) float32 3.571e-09 7.384e-09 ... 4.795e-08 chi (depth, hour, tau_bins) float32 1.417e-08 1.12e-08 ... 1.021e-08 Jb (depth, hour, tau_bins) float32 2.363e-10 2.037e-10 ... 8.176e-10 Jq (depth, hour, tau_bins) float64 -0.3639 -0.3232 ... -34.57 -7.167 S2 (depth, hour, tau_bins) float32 1.608e-05 3.475e-05 ... 4.999e-05 N2 (depth, hour, tau_bins) float32 0.0002237 0.0002014 ... 1.187e-05 Rig (depth, hour, tau_bins) float32 13.2 4.297 3.056 ... 0.2232 0.2512 Rig_T (depth, hour, tau_bins) float32 13.39 4.173 ... 0.1836 0.2279 tau (hour, tau_bins) float32 0.02931 0.05352 0.0824 ... 0.0542 0.08266 Attributes: title: KPP ν0=2.5, Ric=0.2, Ri0=0.5baseline.kpp.lmd.004- depth: 6
- hour: 24
- tau_bins: 3
- depth(depth)float64-89.0 -69.0 -59.0 -49.0 -39.0 -29.0
- axis :
- Z
- long_name :
- Interface pseudo-depth, -z*
- positive :
- up
- units :
- meter
array([-89., -69., -59., -49., -39., -29.])
- xh()float64-140.0
- axis :
- X
- domain_decomposition :
- [220, 222, 220, 221]
- long_name :
- h point nominal longitude
- units :
- degrees_east
array(-140.)
- yh()float640.0625
- axis :
- Y
- domain_decomposition :
- [210, 258, 210, 221]
- long_name :
- h point nominal latitude
- units :
- degrees_north
array(0.06249997)
- yq()float64-0.0625
- axis :
- Y
- domain_decomposition :
- [209, 257, 209, 221]
- long_name :
- q point nominal latitude
- units :
- degrees_north
array(-0.06249997)
- hour(hour)int640 1 2 3 4 5 6 ... 18 19 20 21 22 23
array([ 0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23]) - tau_bins(tau_bins)object(0.0, 0.04] ... (0.075, inf]
- cell_methods :
- yh:mean xq:point time: mean
- interp_method :
- none
- long_name :
- Zonal surface stress from ocean interactions with atmos and ice
- time_avg_info :
- average_T1,average_T2,average_DT
- units :
- Pa
array([Interval(0.0, 0.04, closed='right'), Interval(0.04, 0.075, closed='right'), Interval(0.075, inf, closed='right')], dtype=object)
- KT(depth, hour, tau_bins)float321.001e-06 0.000262 ... 0.0003525
- cell_measures :
- area: areacello
- cell_methods :
- area:mean zi:point yh:mean xh:mean time: mean
- long_name :
- Total diapycnal diffusivity for heat at interfaces
- standard_name :
- ocean_vertical_heat_diffusivity
- time_avg_info :
- average_T1,average_T2,average_DT
- units :
- m2 s-1
array([[[1.00062653e-06, 2.62037240e-04, 1.05890748e-03], [1.00062664e-06, 2.72701523e-04, 1.09908008e-03], [1.00062664e-06, 2.90863565e-04, 1.02824823e-03], [1.00062664e-06, 2.96896702e-04, 1.00071402e-03], [1.00062664e-06, 2.66355200e-04, 1.02508185e-03], [1.00062653e-06, 2.53402599e-04, 9.46604297e-04], [1.00062653e-06, 2.26842472e-04, 8.61362787e-04], [1.00062653e-06, 2.20032278e-04, 8.83506727e-04], [1.00062664e-06, 2.11917271e-04, 8.30798468e-04], [1.00062664e-06, 2.03725998e-04, 8.30619712e-04], [1.00062675e-06, 1.92588341e-04, 8.49988079e-04], [1.00062675e-06, 1.90688588e-04, 8.54582933e-04], [1.00062675e-06, 1.91088926e-04, 7.87007099e-04], [1.00062664e-06, 1.57940449e-04, 7.20337499e-04], [1.00062653e-06, 1.38884861e-04, 6.53320050e-04], [1.00062653e-06, 1.24576676e-04, 6.12310017e-04], [1.00062653e-06, 1.00052304e-04, 7.07846077e-04], [1.00062641e-06, 9.21100873e-05, 7.37336930e-04], [1.00062641e-06, 8.17428736e-05, 7.50365201e-04], [1.00062641e-06, 1.24435348e-04, 8.18357163e-04], ... [1.41367852e-03, 1.79877831e-03, 1.71377081e-02], [1.37466111e-03, 2.30275956e-03, 3.32856625e-02], [1.34840910e-03, 3.03058885e-03, 4.47302125e-02], [1.38495094e-03, 4.78022080e-03, 5.33157960e-02], [1.47192599e-03, 9.51274391e-03, 5.98784201e-02], [1.55525003e-03, 1.41898468e-02, 6.55668080e-02], [1.60700711e-03, 1.95938740e-02, 7.16438368e-02], [1.77496276e-03, 2.32385416e-02, 7.43482858e-02], [1.91565300e-03, 2.69467719e-02, 7.60267600e-02], [1.93555490e-03, 2.79643107e-02, 7.59711117e-02], [2.08756095e-03, 2.94796471e-02, 7.63885081e-02], [2.20756372e-03, 3.05341445e-02, 7.48512000e-02], [2.07593641e-03, 3.59162269e-03, 2.28384528e-02], [2.05276557e-03, 1.21652649e-03, 2.78259465e-03], [2.36642803e-03, 1.12493162e-03, 2.53933412e-03], [2.31716968e-03, 1.69391045e-03, 1.25961495e-03], [2.22260458e-03, 1.90275419e-03, 6.10022515e-04], [2.06597475e-03, 1.90889882e-03, 6.11530559e-04], [1.90941000e-03, 1.74662611e-03, 4.70483530e-04], [1.74895767e-03, 1.62311806e-03, 3.52516159e-04]]], dtype=float32) - eps(depth, hour, tau_bins)float324.898e-08 2.109e-07 ... 3.369e-08
- long_name :
- $ε$
- units :
- W/kg
array([[[4.89753944e-08, 2.10869956e-07, 3.65941844e-07], [5.13541139e-08, 2.15127756e-07, 3.64754300e-07], [5.41888241e-08, 2.15065000e-07, 3.46860901e-07], [5.61319773e-08, 2.09817756e-07, 3.30694377e-07], [5.58599709e-08, 2.04127033e-07, 3.10800772e-07], [5.33404716e-08, 1.98032964e-07, 2.99553619e-07], [5.17132221e-08, 1.91185251e-07, 2.81550996e-07], [5.10814289e-08, 1.86589659e-07, 2.75198403e-07], [5.25533004e-08, 1.82796654e-07, 2.59321155e-07], [5.27817008e-08, 1.74193303e-07, 2.56438966e-07], [5.35027027e-08, 1.69497440e-07, 2.46893592e-07], [5.34857207e-08, 1.68255823e-07, 2.44179517e-07], [5.52743167e-08, 1.66044600e-07, 2.39957984e-07], [5.02844699e-08, 1.62360294e-07, 2.42779464e-07], [4.44484414e-08, 1.55758229e-07, 2.45422200e-07], [4.25727400e-08, 1.50236517e-07, 2.38815062e-07], [3.93804953e-08, 1.47907002e-07, 2.66488684e-07], [3.63328496e-08, 1.53234055e-07, 2.91657159e-07], [3.25132916e-08, 1.56820207e-07, 3.12330428e-07], [3.14434097e-08, 1.66755285e-07, 3.35571485e-07], ... [1.32510735e-07, 2.67840505e-07, 6.84367080e-07], [1.40816013e-07, 4.82158498e-07, 8.39150061e-07], [1.43348117e-07, 6.31026808e-07, 7.57897965e-07], [1.52440421e-07, 7.13276108e-07, 6.29790861e-07], [1.71569596e-07, 6.93433947e-07, 5.20004505e-07], [1.98224043e-07, 6.28771090e-07, 4.44837781e-07], [2.26603930e-07, 5.59493174e-07, 3.89336378e-07], [2.64661935e-07, 4.87133264e-07, 3.41043062e-07], [2.79234030e-07, 4.23746258e-07, 3.01816016e-07], [2.74314488e-07, 3.87589012e-07, 2.78628193e-07], [2.67226142e-07, 3.57912597e-07, 2.75003202e-07], [2.66750249e-07, 3.30479708e-07, 2.65963621e-07], [2.28383229e-07, 1.10453826e-07, 1.06349248e-07], [2.19336044e-07, 7.44121991e-08, 2.20961454e-08], [2.48404348e-07, 1.06142551e-07, 2.67914810e-08], [2.43364752e-07, 1.25137674e-07, 2.28767245e-08], [2.34714150e-07, 1.32180332e-07, 2.11699458e-08], [2.13275101e-07, 1.27884704e-07, 2.26537828e-08], [1.95051314e-07, 1.19670261e-07, 2.58323478e-08], [1.77437926e-07, 1.09955913e-07, 3.36903945e-08]]], dtype=float32) - chi(depth, hour, tau_bins)float326.284e-08 2.881e-07 ... 6.942e-09
- long_name :
- $χ$
- units :
- C^2/s
array([[[6.28427301e-08, 2.88068264e-07, 8.26305950e-07], [6.27368166e-08, 2.98960629e-07, 8.71046439e-07], [6.44347011e-08, 3.12472736e-07, 9.56297868e-07], [6.58234427e-08, 2.90687467e-07, 8.94805169e-07], [6.38682138e-08, 2.69036633e-07, 8.48512173e-07], [6.23498408e-08, 2.60920700e-07, 7.95632900e-07], [6.17878939e-08, 2.35277298e-07, 7.43213093e-07], [6.09875315e-08, 2.30552388e-07, 7.15444685e-07], [6.19845935e-08, 2.33280574e-07, 6.38697657e-07], [6.30164294e-08, 2.23609277e-07, 6.13384714e-07], [6.33949782e-08, 2.18304905e-07, 6.51640221e-07], [6.44858815e-08, 2.16931255e-07, 6.27009797e-07], [6.66231301e-08, 2.17100066e-07, 5.76267610e-07], [6.13616891e-08, 2.07990112e-07, 5.76540401e-07], [5.89936313e-08, 1.95030083e-07, 5.98523343e-07], [5.78152637e-08, 1.80715574e-07, 5.96839300e-07], [5.59458790e-08, 1.73955854e-07, 6.28465671e-07], [5.44911316e-08, 1.69204824e-07, 6.99216798e-07], [5.31692095e-08, 1.68147437e-07, 7.60493265e-07], [5.20742489e-08, 1.85152899e-07, 7.71945395e-07], ... [8.01801932e-08, 1.43546359e-07, 3.93581416e-07], [8.15358305e-08, 2.63959834e-07, 4.96796190e-07], [8.34458191e-08, 3.63753145e-07, 3.89451685e-07], [9.06807145e-08, 3.98819338e-07, 2.63717595e-07], [9.65297176e-08, 3.69848379e-07, 1.95779862e-07], [1.16798915e-07, 3.11457910e-07, 1.47851480e-07], [1.31431392e-07, 2.54156788e-07, 1.17898125e-07], [1.45972948e-07, 2.09105082e-07, 9.08319109e-08], [1.43596168e-07, 1.64574814e-07, 7.50877760e-08], [1.30332225e-07, 1.36982720e-07, 7.11929431e-08], [1.25370548e-07, 1.19903476e-07, 6.65046258e-08], [1.23980243e-07, 1.06661567e-07, 6.42934452e-08], [1.02193404e-07, 1.57689470e-08, 2.12460556e-08], [1.21662225e-07, 7.79856713e-09, 2.85935986e-09], [1.81704664e-07, 1.52409090e-08, 4.98988229e-09], [1.96759984e-07, 4.02906437e-08, 3.78793130e-09], [1.93130234e-07, 6.06322743e-08, 3.38508843e-09], [1.76792256e-07, 6.97176006e-08, 4.78815654e-09], [1.61031181e-07, 6.62385702e-08, 5.76574166e-09], [1.42380628e-07, 6.12754292e-08, 6.94171742e-09]]], dtype=float32) - Jb(depth, hour, tau_bins)float325.201e-10 9.148e-09 ... 8.505e-10
- cell_measures :
- area: areacello
- cell_methods :
- area:mean zi:point yh:mean xh:mean time: mean
- long_name :
- Total diapycnal diffusivity for heat at interfaces
- standard_name :
- ocean_vertical_diffusive_buoyancy_flux
- time_avg_info :
- average_T1,average_T2,average_DT
- units :
- m2 s-1
array([[[5.20121723e-10, 9.14844023e-09, 4.93047665e-08], [5.22739962e-10, 9.68542224e-09, 5.20738865e-08], [5.26788724e-10, 1.03196767e-08, 5.22399120e-08], [5.31486521e-10, 9.74224612e-09, 5.16224858e-08], [5.28820432e-10, 8.79323636e-09, 4.80655693e-08], [5.22581700e-10, 7.71664954e-09, 4.50965629e-08], [5.21203691e-10, 5.64962477e-09, 4.01015399e-08], [5.18142307e-10, 5.64664893e-09, 3.91272224e-08], [5.23529831e-10, 5.48414247e-09, 3.74587685e-08], [5.28541544e-10, 5.04714226e-09, 3.46567788e-08], [5.26243826e-10, 4.30064651e-09, 3.66826640e-08], [5.30970545e-10, 4.58714178e-09, 3.40342439e-08], [5.40062717e-10, 4.76399675e-09, 3.22511404e-08], [5.22477839e-10, 3.94990529e-09, 3.11737196e-08], [5.14006226e-10, 2.94821079e-09, 3.12277812e-08], [5.08801501e-10, 2.20570762e-09, 3.06338883e-08], [5.04617959e-10, 1.54705970e-09, 3.28621717e-08], [4.98949326e-10, 1.16123022e-09, 3.47503857e-08], [4.88723784e-10, 1.06063713e-09, 3.58807526e-08], [4.87477392e-10, 2.45552156e-09, 3.57378731e-08], ... [1.19724994e-08, 1.70177330e-08, 1.26920753e-07], [1.19211130e-08, 2.91519573e-08, 1.85755169e-07], [1.16928520e-08, 4.86725327e-08, 1.75252964e-07], [1.28964057e-08, 7.18805637e-08, 1.54877227e-07], [1.38640353e-08, 8.52027426e-08, 1.34078221e-07], [1.57190652e-08, 8.47513775e-08, 1.16542580e-07], [1.61323648e-08, 8.39835579e-08, 1.00404989e-07], [1.95944843e-08, 7.88693768e-08, 8.40583709e-08], [1.87968698e-08, 7.23438234e-08, 7.34735437e-08], [1.98578043e-08, 6.28177261e-08, 6.60879067e-08], [1.82452986e-08, 6.05719066e-08, 6.59063346e-08], [2.02872936e-08, 5.50794574e-08, 6.34640145e-08], [1.40410705e-08, 6.71363454e-09, 1.99716030e-08], [1.54548694e-08, 1.65583369e-09, 1.90645566e-09], [2.33801369e-08, 1.92635197e-09, 2.99118907e-09], [2.59499373e-08, 5.00197261e-09, 1.58494307e-09], [2.67266209e-08, 8.41553582e-09, 7.80392917e-10], [2.56543515e-08, 9.63710711e-09, 8.30871316e-10], [2.25431442e-08, 9.36158528e-09, 7.23190507e-10], [2.03528803e-08, 8.93805474e-09, 8.50497450e-10]]], dtype=float32) - Jq(depth, hour, tau_bins)float64-0.8128 -35.11 ... -31.0 -4.416
- units :
- W/m^2
- long_name :
- $J_q^t$
array([[[ -0.81275251, -35.10540508, -107.92516258], [ -0.81452745, -37.30334659, -111.43608918], [ -0.81740106, -38.24310327, -110.81864896], [ -0.8337643 , -36.90952601, -106.07761669], [ -0.83078597, -33.39633727, -102.65993507], [ -0.81278874, -31.48373516, -96.20815795], [ -0.80897951, -29.27580381, -91.31173044], [ -0.80735026, -28.07736327, -86.57831432], [ -0.81581746, -28.11210656, -80.24805898], [ -0.8243209 , -26.31316743, -79.05538436], [ -0.82850863, -24.78306735, -78.83334196], [ -0.83782546, -24.51323225, -76.66792171], [ -0.84537657, -25.62885892, -73.43640519], [ -0.81986974, -22.42427483, -71.26076615], [ -0.79646231, -19.5188711 , -69.76079995], [ -0.78984496, -16.53241175, -67.13581603], [ -0.77231664, -13.41119337, -74.19449757], [ -0.7643023 , -12.92565907, -79.32650121], [ -0.75232554, -11.57467935, -82.422927 ], [ -0.7441352 , -17.67938361, -86.26225594], ... [ -31.87862027, -44.16697882, -261.96079925], [ -31.56887868, -72.83843194, -377.9896837 ], [ -30.9545401 , -128.39360995, -375.99278148], [ -31.56165977, -188.84136826, -353.71039164], [ -35.07868203, -213.64488296, -324.67284683], [ -38.16875777, -216.86511225, -295.0096038 ], [ -40.91438287, -214.01366732, -270.30288383], [ -47.57095843, -204.76566038, -247.3864047 ], [ -51.89456633, -194.27813913, -231.64378862], [ -53.58953487, -177.50533146, -221.23058939], [ -53.27081906, -171.74451817, -210.98684748], [ -57.0413013 , -164.76087878, -205.40908003], [ -40.80732238, -27.55403688, -71.54460267], [ -44.89828358, -8.20514559, -9.65387598], [ -59.21442771, -10.90296723, -11.91643733], [ -59.91662008, -24.30769411, -7.67025163], [ -60.46739161, -32.15060793, -4.7937284 ], [ -55.39888652, -34.30766255, -5.44876747], [ -51.89013205, -32.49015759, -5.0439275 ], [ -46.88159532, -31.00173399, -4.41609556]]]) - S2(depth, hour, tau_bins)float320.0002157 0.0002126 ... 2.707e-05
- long_name :
- $S^2$
- units :
- s$^{-2}$
array([[[2.15672277e-04, 2.12571860e-04, 2.09773949e-04], [2.11442632e-04, 2.13960826e-04, 2.08297279e-04], [2.12287967e-04, 2.13467691e-04, 2.08366328e-04], [2.10965911e-04, 2.11972176e-04, 2.07678633e-04], [2.12011786e-04, 2.13226915e-04, 2.05537624e-04], [2.10400962e-04, 2.13552674e-04, 2.04386102e-04], [2.12897561e-04, 2.12111685e-04, 2.02022580e-04], [2.10545331e-04, 2.11847306e-04, 1.97839865e-04], [2.11565115e-04, 2.10612125e-04, 1.97103727e-04], [2.16023400e-04, 2.07201680e-04, 1.95945642e-04], [2.16643792e-04, 2.06816840e-04, 1.92040781e-04], [2.15596141e-04, 2.06519107e-04, 1.87373429e-04], [2.16904009e-04, 2.03615797e-04, 1.89063925e-04], [2.13125197e-04, 2.04004653e-04, 1.98501541e-04], [2.10339669e-04, 2.04935292e-04, 1.98550842e-04], [2.10460654e-04, 2.03726391e-04, 2.05102784e-04], [2.10142141e-04, 2.03563191e-04, 2.09907332e-04], [2.13177133e-04, 2.04036303e-04, 2.12833940e-04], [2.09707534e-04, 2.06934783e-04, 2.16156070e-04], [2.09469115e-04, 2.09337260e-04, 2.18377172e-04], ... [7.56139125e-05, 8.71341690e-05, 4.80451272e-05], [7.91544371e-05, 1.01554950e-04, 2.81878711e-05], [8.30380959e-05, 1.09442073e-04, 1.88173381e-05], [8.69750729e-05, 1.01881058e-04, 1.30142435e-05], [9.41247126e-05, 7.86011806e-05, 9.69898065e-06], [9.96056042e-05, 5.49317556e-05, 7.44869567e-06], [1.06645661e-04, 3.67982102e-05, 5.92534889e-06], [1.08007203e-04, 2.58306009e-05, 4.90106504e-06], [1.06873224e-04, 1.89379862e-05, 4.29951342e-06], [1.06376858e-04, 1.58985531e-05, 4.05997798e-06], [1.01732832e-04, 1.36065619e-05, 3.86104784e-06], [1.00730147e-04, 1.22374022e-05, 3.82177495e-06], [1.14109302e-04, 2.29600610e-05, 3.37873757e-06], [1.07913307e-04, 4.26053302e-05, 4.52991026e-06], [1.08090324e-04, 5.52821584e-05, 5.62377090e-06], [1.04775114e-04, 5.55655897e-05, 7.24206393e-06], [1.01420417e-04, 5.40483234e-05, 1.19902697e-05], [9.72153139e-05, 5.22391856e-05, 1.74126035e-05], [9.16627687e-05, 5.07932564e-05, 2.12584673e-05], [8.66877963e-05, 5.03534393e-05, 2.70670953e-05]]], dtype=float32) - N2(depth, hour, tau_bins)float320.0002498 0.0001501 ... 1.074e-05
- cell_measures :
- area: areacello
- cell_methods :
- area:mean zi:point yh:mean xh:mean time: mean
- long_name :
- Buoyancy frequency squared
- time_avg_info :
- average_T1,average_T2,average_DT
- units :
- s-2
array([[[2.49770761e-04, 1.50098756e-04, 1.11120258e-04], [2.47220974e-04, 1.48095016e-04, 1.09469358e-04], [2.45606410e-04, 1.47212602e-04, 1.16273659e-04], [2.44910567e-04, 1.46733626e-04, 1.18762495e-04], [2.46512733e-04, 1.49197745e-04, 1.12651309e-04], [2.48484663e-04, 1.50325548e-04, 1.12898553e-04], [2.50331883e-04, 1.52102992e-04, 1.12781214e-04], [2.47574528e-04, 1.54107634e-04, 1.07344546e-04], [2.44933646e-04, 1.54900685e-04, 1.09973000e-04], [2.42529481e-04, 1.55066184e-04, 1.11506415e-04], [2.43119634e-04, 1.55268455e-04, 1.09090055e-04], [2.40484907e-04, 1.56507798e-04, 1.07217173e-04], [2.42932118e-04, 1.54143461e-04, 1.09352390e-04], [2.47160351e-04, 1.55096990e-04, 1.11676884e-04], [2.48186872e-04, 1.56149719e-04, 1.17096948e-04], [2.47517542e-04, 1.57528339e-04, 1.21044686e-04], [2.51487305e-04, 1.58637442e-04, 1.20155579e-04], [2.54361890e-04, 1.61505086e-04, 1.20364450e-04], [2.58087355e-04, 1.62304452e-04, 1.22957426e-04], [2.59101274e-04, 1.60314288e-04, 1.22088502e-04], ... [2.85044662e-05, 2.38834564e-05, 1.88644735e-05], [2.91151373e-05, 2.64380724e-05, 1.56137939e-05], [2.97974948e-05, 2.77846120e-05, 1.16638857e-05], [3.07708033e-05, 2.75006969e-05, 8.30140198e-06], [3.24869034e-05, 2.40745630e-05, 6.47047591e-06], [3.33005883e-05, 1.96775745e-05, 5.33579077e-06], [3.45363878e-05, 1.50565565e-05, 4.39064070e-06], [3.53112300e-05, 1.14167287e-05, 3.83091219e-06], [3.45137305e-05, 8.98336566e-06, 3.45510307e-06], [3.41937484e-05, 7.86413875e-06, 3.39021835e-06], [3.30625990e-05, 7.16474597e-06, 3.19372793e-06], [3.16721853e-05, 6.60015530e-06, 3.07497498e-06], [3.25626825e-05, 6.08912433e-06, 2.91151355e-06], [3.47301902e-05, 7.46536080e-06, 2.89518221e-06], [3.47736095e-05, 1.20941550e-05, 3.61292769e-06], [3.44612490e-05, 1.47121618e-05, 4.20345714e-06], [3.39114413e-05, 1.61574008e-05, 5.35977597e-06], [3.25952788e-05, 1.72388645e-05, 7.28179202e-06], [3.15559228e-05, 1.78157061e-05, 8.98511098e-06], [3.07502341e-05, 1.84755536e-05, 1.07377618e-05]]], dtype=float32) - Rig(depth, hour, tau_bins)float320.6921 0.535 ... 0.4047 0.4431
- cell_measures :
- area: areacello
- cell_methods :
- area:mean zi:point yh:mean xh:mean time: mean
- long_name :
- $Ri^g$
- time_avg_info :
- average_T1,average_T2,average_DT
array([[[0.6920738 , 0.5349543 , 0.4580132 ], [0.6879399 , 0.5314694 , 0.4592346 ], [0.6806804 , 0.5308178 , 0.4657764 ], [0.6791985 , 0.53030324, 0.47593078], [0.6806549 , 0.5339809 , 0.4767241 ], [0.68695664, 0.53564453, 0.48169115], [0.6896516 , 0.53775823, 0.49365795], [0.6888744 , 0.5401193 , 0.49436718], [0.68251336, 0.5424917 , 0.5011655 ], [0.68021417, 0.54622084, 0.5020577 ], [0.6766766 , 0.5488351 , 0.5055933 ], [0.6775784 , 0.55120665, 0.5052763 ], [0.6728198 , 0.55457515, 0.5086805 ], [0.6839738 , 0.55721325, 0.51228535], [0.691461 , 0.5597189 , 0.5188104 ], [0.69568074, 0.56400734, 0.52135473], [0.7045409 , 0.56998813, 0.5013685 ], [0.7121001 , 0.56713057, 0.49566406], [0.72767735, 0.56552804, 0.490233 ], [0.7288958 , 0.5595642 , 0.47649854], ... [0.40634552, 0.33188626, 0.5003445 ], [0.39810854, 0.3208425 , 0.581831 ], [0.39363727, 0.32485807, 0.6355092 ], [0.38015234, 0.34808362, 0.66806245], [0.36744684, 0.39145422, 0.6928216 ], [0.35197896, 0.42392755, 0.7202125 ], [0.34439027, 0.46392506, 0.74636894], [0.34399074, 0.4974593 , 0.77452666], [0.3449704 , 0.5331218 , 0.7831469 ], [0.34179208, 0.54510725, 0.80061936], [0.34201652, 0.5604679 , 0.8105681 ], [0.34889582, 0.5762068 , 0.81085813], [0.2864733 , 0.38954324, 0.84244406], [0.31387162, 0.28162244, 0.6502222 ], [0.32518497, 0.30333692, 0.62802637], [0.3409173 , 0.334327 , 0.56850284], [0.3572793 , 0.35580266, 0.46713182], [0.36969233, 0.37518197, 0.453887 ], [0.37960896, 0.39090776, 0.44790077], [0.39049977, 0.40465185, 0.44312316]]], dtype=float32) - Rig_T(depth, hour, tau_bins)float320.6525 0.4943 ... 0.3079 0.3627
- long_name :
- $Ri^g_T$
array([[[0.6524795 , 0.49431792, 0.40401042], [0.6476281 , 0.49186328, 0.40452516], [0.6441446 , 0.4895203 , 0.4143563 ], [0.64207554, 0.49051276, 0.41974872], [0.6392752 , 0.49562863, 0.4259835 ], [0.6485602 , 0.49845082, 0.43141162], [0.65098137, 0.50073636, 0.43876797], [0.6474688 , 0.50299937, 0.4344629 ], [0.64145947, 0.50219667, 0.44302076], [0.63325673, 0.5045558 , 0.44308442], [0.6337309 , 0.50653815, 0.44803062], [0.63019466, 0.5095291 , 0.45472556], [0.630605 , 0.50949144, 0.4569693 ], [0.6359448 , 0.51431185, 0.46192995], [0.64430714, 0.5192264 , 0.472704 ], [0.65253395, 0.52388585, 0.47870392], [0.6600066 , 0.52851456, 0.4612955 ], [0.6755862 , 0.5250453 , 0.45920655], [0.68343335, 0.52443343, 0.45433536], [0.6843606 , 0.52000076, 0.43686157], ... [0.29926416, 0.23857142, 0.26230258], [0.2974115 , 0.21804348, 0.30031466], [0.28810856, 0.21146052, 0.32518813], [0.28323996, 0.21127589, 0.3577328 ], [0.26677144, 0.2186438 , 0.38158107], [0.2535754 , 0.23513977, 0.39806974], [0.24205409, 0.25546497, 0.40978163], [0.2332109 , 0.2754921 , 0.42806375], [0.22958612, 0.2987565 , 0.43636626], [0.23360637, 0.306973 , 0.4462604 ], [0.23348331, 0.31307265, 0.45968074], [0.23676656, 0.32316506, 0.46267766], [0.22447279, 0.25387365, 0.5430862 ], [0.24416187, 0.2449134 , 0.4584749 ], [0.2532297 , 0.24663666, 0.46583867], [0.26291543, 0.25914267, 0.46689865], [0.27117586, 0.27355063, 0.43329036], [0.28200358, 0.28652632, 0.39690372], [0.28587258, 0.29844803, 0.38097277], [0.29391518, 0.30785322, 0.36271706]]], dtype=float32) - tau(hour, tau_bins)float320.02913 0.05397 ... 0.05443 0.08317
- cell_methods :
- yh:mean xq:point time: mean
- interp_method :
- none
- long_name :
- Zonal surface stress from ocean interactions with atmos and ice
- time_avg_info :
- average_T1,average_T2,average_DT
- units :
- Pa
array([[0.02912814, 0.05397057, 0.08243489], [0.02951978, 0.05395206, 0.08258021], [0.02955842, 0.0539168 , 0.08289316], [0.02935282, 0.05389314, 0.08354414], [0.02921309, 0.05400709, 0.083749 ], [0.02929978, 0.05425711, 0.0844179 ], [0.02939584, 0.05473844, 0.08448129], [0.02932024, 0.05432779, 0.08426537], [0.02936672, 0.05443417, 0.08411882], [0.02933902, 0.05419946, 0.08512807], [0.0292527 , 0.05430375, 0.08504955], [0.02921882, 0.05419347, 0.08442991], [0.02883983, 0.05426022, 0.08442935], [0.02913588, 0.05487758, 0.08464634], [0.02929619, 0.05555559, 0.08535865], [0.02973468, 0.0561522 , 0.08564808], [0.02978581, 0.05643295, 0.08602639], [0.0299926 , 0.05669548, 0.08611111], [0.0300919 , 0.05693631, 0.08645762], [0.02996605, 0.05639978, 0.08547585], [0.02971533, 0.05582306, 0.08512087], [0.02977535, 0.05557946, 0.08559605], [0.0296651 , 0.05494839, 0.08416149], [0.02947801, 0.05443047, 0.08317043]], dtype=float32)
- title :
- KD=0, KV=0
<xarray.DatasetView> Dimensions: (depth: 6, hour: 24, tau_bins: 3) Coordinates: * depth (depth) float64 -89.0 -69.0 -59.0 -49.0 -39.0 -29.0 xh float64 -140.0 yh float64 0.0625 yq float64 -0.0625 * hour (hour) int64 0 1 2 3 4 5 6 7 8 9 ... 14 15 16 17 18 19 20 21 22 23 * tau_bins (tau_bins) object (0.0, 0.04] (0.04, 0.075] (0.075, inf] Data variables: KT (depth, hour, tau_bins) float32 1.001e-06 0.000262 ... 0.0003525 eps (depth, hour, tau_bins) float32 4.898e-08 2.109e-07 ... 3.369e-08 chi (depth, hour, tau_bins) float32 6.284e-08 2.881e-07 ... 6.942e-09 Jb (depth, hour, tau_bins) float32 5.201e-10 9.148e-09 ... 8.505e-10 Jq (depth, hour, tau_bins) float64 -0.8128 -35.11 ... -31.0 -4.416 S2 (depth, hour, tau_bins) float32 0.0002157 0.0002126 ... 2.707e-05 N2 (depth, hour, tau_bins) float32 0.0002498 0.0001501 ... 1.074e-05 Rig (depth, hour, tau_bins) float32 0.6921 0.535 ... 0.4047 0.4431 Rig_T (depth, hour, tau_bins) float32 0.6525 0.4943 ... 0.3079 0.3627 tau (hour, tau_bins) float32 0.02913 0.05397 ... 0.05443 0.08317 Attributes: title: KD=0, KV=0new_baseline.hb- depth: 6
- hour: 24
- tau_bins: 3
- depth(depth)float64-89.0 -69.0 -59.0 -49.0 -39.0 -29.0
- axis :
- Z
- long_name :
- Interface pseudo-depth, -z*
- positive :
- up
- units :
- meter
array([-89., -69., -59., -49., -39., -29.])
- xh()float64-140.0
- axis :
- X
- domain_decomposition :
- [220, 222, 220, 221]
- long_name :
- h point nominal longitude
- units :
- degrees_east
array(-140.)
- yh()float640.0625
- axis :
- Y
- domain_decomposition :
- [210, 258, 210, 221]
- long_name :
- h point nominal latitude
- units :
- degrees_north
array(0.06249997)
- yq()float64-0.0625
- axis :
- Y
- domain_decomposition :
- [209, 257, 209, 221]
- long_name :
- q point nominal latitude
- units :
- degrees_north
array(-0.06249997)
- hour(hour)int640 1 2 3 4 5 6 ... 18 19 20 21 22 23
array([ 0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23]) - tau_bins(tau_bins)object(0.0, 0.04] ... (0.075, inf]
- cell_methods :
- yh:mean xq:point time: mean
- interp_method :
- none
- long_name :
- Zonal surface stress from ocean interactions with atmos and ice
- time_avg_info :
- average_T1,average_T2,average_DT
- units :
- Pa
array([Interval(0.0, 0.04, closed='right'), Interval(0.04, 0.075, closed='right'), Interval(0.075, inf, closed='right')], dtype=object)
- KT(depth, hour, tau_bins)float321.001e-06 1.001e-06 ... 0.0001793
- cell_measures :
- area: areacello
- cell_methods :
- area:mean zi:point yh:mean xh:mean time: mean
- long_name :
- Total diapycnal diffusivity for heat at interfaces
- standard_name :
- ocean_vertical_heat_diffusivity
- time_avg_info :
- average_T1,average_T2,average_DT
- units :
- m2 s-1
array([[[1.0006263e-06, 1.0006262e-06, 1.0006262e-06], [1.0006263e-06, 1.0006262e-06, 1.0006262e-06], [1.0006263e-06, 1.0006262e-06, 1.0006262e-06], [1.0006263e-06, 1.0006262e-06, 1.0006262e-06], [1.0006263e-06, 1.0006262e-06, 1.0006262e-06], [1.0006263e-06, 1.0006262e-06, 1.0006262e-06], [1.0006263e-06, 1.0006262e-06, 1.0006262e-06], [1.0006263e-06, 1.0006262e-06, 1.0006262e-06], [1.0006263e-06, 1.0006262e-06, 1.0006262e-06], [1.0006263e-06, 1.0006262e-06, 1.0006262e-06], [1.0006263e-06, 1.0006262e-06, 1.0006262e-06], [1.0006263e-06, 1.0006262e-06, 1.0006262e-06], [1.0006263e-06, 1.0006262e-06, 1.0006262e-06], [1.0006263e-06, 1.0006262e-06, 1.0006262e-06], [1.0006263e-06, 1.0006262e-06, 1.0006262e-06], [1.0006263e-06, 1.0006262e-06, 1.0006262e-06], [1.0006263e-06, 1.0006262e-06, 1.0006262e-06], [1.0006263e-06, 1.0006262e-06, 1.0006262e-06], [1.0006263e-06, 1.0006263e-06, 1.0006262e-06], [1.0006263e-06, 1.0006262e-06, 1.0006262e-06], ... [9.4132446e-04, 1.0125230e-03, 3.3146515e-03], [9.1822050e-04, 1.1766625e-03, 1.4005989e-02], [9.2203892e-04, 1.2403573e-03, 2.3555636e-02], [9.2468399e-04, 1.4708596e-03, 3.6420863e-02], [9.4251416e-04, 1.7338136e-03, 4.3797132e-02], [9.6162333e-04, 3.1932166e-03, 4.8042994e-02], [9.9941925e-04, 6.7671593e-03, 5.3763784e-02], [1.0284228e-03, 1.0356332e-02, 5.6218788e-02], [1.0461721e-03, 1.3733745e-02, 5.8365785e-02], [1.0795848e-03, 1.4616154e-02, 5.8618966e-02], [1.1059251e-03, 1.5877362e-02, 5.8043797e-02], [1.1142871e-03, 1.6551722e-02, 5.5786945e-02], [1.2179909e-03, 1.7418606e-03, 1.4232323e-02], [1.2699188e-03, 1.2363050e-03, 6.4157473e-04], [1.3006497e-03, 1.2935506e-03, 7.0208183e-04], [1.2927618e-03, 1.3108684e-03, 2.6574192e-04], [1.2749400e-03, 1.2415671e-03, 2.4541048e-04], [1.2317502e-03, 1.1815999e-03, 2.0568147e-04], [1.1754853e-03, 1.1246946e-03, 2.0106597e-04], [1.1240497e-03, 1.0682463e-03, 1.7925282e-04]]], dtype=float32) - eps(depth, hour, tau_bins)float326.182e-10 1.664e-09 ... 1.489e-08
- long_name :
- $ε$
- units :
- W/kg
array([[[6.18212148e-10, 1.66353620e-09, 6.16506490e-09], [6.21017682e-10, 1.67842806e-09, 9.27319999e-09], [6.21645957e-10, 1.73616943e-09, 1.19016654e-08], [6.23542662e-10, 1.70288716e-09, 1.31447342e-08], [6.13696038e-10, 1.66104730e-09, 1.81335498e-08], [5.88845916e-10, 1.63481761e-09, 1.95871586e-08], [5.78864401e-10, 1.61448899e-09, 1.34869200e-08], [5.73052494e-10, 1.55730606e-09, 2.45271714e-08], [5.76379722e-10, 1.56207058e-09, 2.89932629e-08], [5.83151361e-10, 1.56888014e-09, 2.43189096e-08], [5.94897742e-10, 1.52692259e-09, 2.98047134e-08], [5.96837024e-10, 1.54409485e-09, 2.83610735e-08], [6.04147454e-10, 1.57005697e-09, 2.05234958e-08], [5.86772519e-10, 1.54123359e-09, 1.26474884e-08], [5.83876281e-10, 1.46456780e-09, 1.18291696e-08], [5.70010428e-10, 1.43973800e-09, 7.06141190e-09], [5.60596181e-10, 1.38091871e-09, 7.66254438e-09], [5.49527090e-10, 1.35822509e-09, 8.44123882e-09], [5.54969071e-10, 1.33498079e-09, 6.55933352e-09], [5.56361068e-10, 1.39136047e-09, 1.06588587e-08], ... [1.69150908e-07, 2.43112993e-07, 9.39682195e-07], [1.69089219e-07, 2.83000759e-07, 1.08108452e-06], [1.69189576e-07, 4.53984995e-07, 1.09987320e-06], [1.76490246e-07, 6.83876010e-07, 1.02829711e-06], [1.87332532e-07, 8.34422167e-07, 8.87234876e-07], [2.03888462e-07, 8.63935895e-07, 7.73098748e-07], [2.31252272e-07, 8.49893922e-07, 6.49428159e-07], [2.59941800e-07, 7.81794597e-07, 5.79500636e-07], [2.96473843e-07, 7.32776357e-07, 5.20611252e-07], [3.03595698e-07, 6.56302234e-07, 4.67348343e-07], [3.09861889e-07, 6.14048531e-07, 4.71917417e-07], [3.30604593e-07, 5.71120836e-07, 4.46970546e-07], [2.76740167e-07, 2.10724153e-07, 1.51785173e-07], [2.83532700e-07, 1.67954241e-07, 2.14249205e-08], [2.78899847e-07, 1.95938682e-07, 3.27067085e-08], [2.78074339e-07, 2.04555377e-07, 2.03654213e-08], [2.68917859e-07, 1.91863649e-07, 1.66435079e-08], [2.55161297e-07, 1.72959460e-07, 1.53984452e-08], [2.40075252e-07, 1.58456729e-07, 1.62956582e-08], [2.23403930e-07, 1.48273898e-07, 1.48935024e-08]]], dtype=float32) - chi(depth, hour, tau_bins)float321.739e-08 2.265e-08 ... 2.921e-09
- long_name :
- $χ$
- units :
- C^2/s
array([[[1.73884356e-08, 2.26525483e-08, 2.44861411e-08], [1.74541945e-08, 2.25107541e-08, 2.62088093e-08], [1.74165553e-08, 2.30953781e-08, 2.58756945e-08], [1.76193122e-08, 2.29807107e-08, 2.62177746e-08], [1.75198185e-08, 2.28707417e-08, 2.60189026e-08], [1.77797759e-08, 2.27358683e-08, 2.63329873e-08], [1.77949939e-08, 2.27717489e-08, 2.54955133e-08], [1.77553900e-08, 2.28247217e-08, 2.53485677e-08], [1.76991506e-08, 2.27228103e-08, 2.53738861e-08], [1.76896275e-08, 2.26282602e-08, 2.46510261e-08], [1.72673751e-08, 2.28223289e-08, 2.49068179e-08], [1.70976175e-08, 2.28065726e-08, 2.59466173e-08], [1.74429111e-08, 2.25265229e-08, 2.53189221e-08], [1.71922050e-08, 2.24622845e-08, 2.49400109e-08], [1.72470962e-08, 2.21477912e-08, 2.48894487e-08], [1.71766423e-08, 2.19773977e-08, 2.52571652e-08], [1.71460073e-08, 2.18138965e-08, 2.48438727e-08], [1.68117325e-08, 2.15464286e-08, 2.56811177e-08], [1.70101799e-08, 2.12656666e-08, 2.53530850e-08], [1.67263465e-08, 2.17167706e-08, 2.57135699e-08], ... [1.75568431e-07, 1.69639179e-07, 3.88403151e-07], [1.75191545e-07, 1.87313177e-07, 5.02682212e-07], [1.78678505e-07, 2.76781094e-07, 4.66718802e-07], [1.79366026e-07, 3.55949112e-07, 4.29998522e-07], [1.93214674e-07, 4.01829709e-07, 3.54981921e-07], [2.06669668e-07, 3.73105195e-07, 2.72762577e-07], [2.30700508e-07, 3.66185759e-07, 2.02141905e-07], [2.37789180e-07, 3.12547570e-07, 1.59825987e-07], [2.32979346e-07, 2.58506304e-07, 1.35901701e-07], [2.31390715e-07, 2.30672811e-07, 1.19482976e-07], [2.24134709e-07, 2.02506129e-07, 1.22481609e-07], [2.03919456e-07, 1.81497555e-07, 1.22820964e-07], [2.27858976e-07, 2.54538524e-08, 2.69130194e-08], [2.34801988e-07, 3.19046833e-08, 1.72959358e-09], [2.60971547e-07, 7.96455524e-08, 3.60839358e-09], [2.78977524e-07, 9.71027134e-08, 2.21884200e-09], [2.79261258e-07, 9.98343097e-08, 2.69382872e-09], [2.76931189e-07, 1.00610364e-07, 2.64924727e-09], [2.61743025e-07, 9.72737908e-08, 2.82410162e-09], [2.45267103e-07, 9.24204926e-08, 2.92052182e-09]]], dtype=float32) - Jb(depth, hour, tau_bins)float322.555e-10 2.914e-10 ... 2.291e-11
- cell_measures :
- area: areacello
- cell_methods :
- area:mean zi:point yh:mean xh:mean time: mean
- long_name :
- Total diapycnal diffusivity for heat at interfaces
- standard_name :
- ocean_vertical_diffusive_buoyancy_flux
- time_avg_info :
- average_T1,average_T2,average_DT
- units :
- m2 s-1
array([[[2.55470034e-10, 2.91401792e-10, 3.19249349e-10], [2.56496713e-10, 2.91035779e-10, 3.23981342e-10], [2.53829818e-10, 2.94654967e-10, 3.14947873e-10], [2.55533705e-10, 2.92805835e-10, 3.09960974e-10], [2.56380861e-10, 2.91983326e-10, 3.10544312e-10], [2.56754840e-10, 2.91529800e-10, 3.07330911e-10], [2.56076688e-10, 2.91074637e-10, 3.00592301e-10], [2.54780752e-10, 2.91799696e-10, 3.05651116e-10], [2.57439070e-10, 2.91535185e-10, 3.07487813e-10], [2.56943383e-10, 2.93832736e-10, 3.00166780e-10], [2.55828830e-10, 2.93334773e-10, 3.05036496e-10], [2.54726990e-10, 2.92834201e-10, 3.05739545e-10], [2.56902666e-10, 2.92254110e-10, 3.07121273e-10], [2.54530674e-10, 2.91644903e-10, 2.97666614e-10], [2.55004962e-10, 2.89583413e-10, 3.04066772e-10], [2.52027094e-10, 2.88339130e-10, 3.08847586e-10], [2.50817089e-10, 2.87278257e-10, 3.02292469e-10], [2.49159637e-10, 2.86964563e-10, 3.07309900e-10], [2.51137861e-10, 2.85814983e-10, 3.09010206e-10], [2.47247639e-10, 2.86292767e-10, 3.14035603e-10], ... [1.62041829e-08, 1.45443124e-08, 6.74942626e-08], [1.65704819e-08, 1.85746529e-08, 1.38406989e-07], [1.66631597e-08, 2.50191388e-08, 1.54171133e-07], [1.78619324e-08, 3.59505563e-08, 1.83110956e-07], [1.81895565e-08, 5.13915772e-08, 1.60862925e-07], [1.90937630e-08, 6.16821083e-08, 1.43145357e-07], [2.18083027e-08, 7.16589668e-08, 1.28102954e-07], [2.29776802e-08, 7.47350626e-08, 1.13467706e-07], [2.24974031e-08, 7.14504864e-08, 1.11496405e-07], [2.32104789e-08, 6.69645033e-08, 1.04462508e-07], [2.28019950e-08, 6.31832933e-08, 1.03584043e-07], [2.19223555e-08, 6.01868848e-08, 1.01363128e-07], [2.10841904e-08, 9.55064028e-09, 2.09327062e-08], [2.02724735e-08, 3.44046680e-09, 6.70337619e-10], [2.20876579e-08, 6.45958576e-09, 1.19146870e-09], [2.26566019e-08, 9.40468858e-09, 4.07481243e-10], [2.29477024e-08, 9.73539294e-09, 2.40757192e-10], [2.31211477e-08, 9.75570824e-09, 1.00751248e-10], [2.25524239e-08, 9.54282964e-09, 8.35792408e-11], [2.11913314e-08, 9.55753965e-09, 2.29118079e-11]]], dtype=float32) - Jq(depth, hour, tau_bins)float64-0.4038 -0.4617 ... -30.73 -1.899
- units :
- W/m^2
- long_name :
- $J_q^t$
array([[[ -0.40383201, -0.46170693, -0.47979607], [ -0.40629385, -0.46081924, -0.49478992], [ -0.40548916, -0.46611381, -0.48753303], [ -0.40707212, -0.46448855, -0.49537153], [ -0.40654759, -0.46543408, -0.49638149], [ -0.40767024, -0.46317764, -0.5001483 ], [ -0.40825707, -0.46347099, -0.48866422], [ -0.40965568, -0.46438217, -0.48843255], [ -0.40948291, -0.46580355, -0.48770104], [ -0.40507042, -0.46631911, -0.48339054], [ -0.40664369, -0.46244279, -0.48619947], [ -0.39999109, -0.46313942, -0.49600014], [ -0.40572711, -0.46118047, -0.48885789], [ -0.40192261, -0.46193756, -0.47936888], [ -0.40096163, -0.45710388, -0.48437711], [ -0.39976277, -0.45286841, -0.4927702 ], [ -0.40124605, -0.452229 , -0.48249731], [ -0.39830072, -0.45048057, -0.49243466], [ -0.40160454, -0.44609005, -0.4880124 ], [ -0.39683667, -0.45253467, -0.49126503], ... [ -38.01416094, -37.71273213, -146.00726222], [ -38.08362599, -42.7750946 , -298.59354522], [ -37.79889323, -56.78291587, -339.93163012], [ -38.22386721, -86.86501178, -375.49648732], [ -39.24619021, -127.04311749, -336.79308851], [ -41.1660171 , -157.00324937, -323.79881367], [ -44.60286042, -176.94763065, -294.54638861], [ -48.23935169, -180.63089509, -263.29843897], [ -49.37562729, -176.74255824, -248.19471826], [ -52.69931838, -165.18010348, -235.23162031], [ -53.43977151, -160.63160397, -234.23988518], [ -53.31057096, -152.06274918, -227.99312568], [ -49.36203681, -33.31373377, -62.13097584], [ -50.61750746, -17.60716132, -4.09462887], [ -53.39291811, -30.18945973, -5.85316103], [ -55.27374453, -34.94254046, -2.91233767], [ -55.69251915, -34.25285055, -2.41855112], [ -54.34907925, -32.83374329, -2.14437555], [ -52.16474975, -31.96696915, -2.31426363], [ -48.46496735, -30.72726698, -1.89948483]]]) - S2(depth, hour, tau_bins)float326.214e-05 0.0002147 ... 3.608e-05
- long_name :
- $S^2$
- units :
- s$^{-2}$
array([[[6.2135194e-05, 2.1472209e-04, 3.2808020e-04], [6.2408239e-05, 2.1617881e-04, 3.8527348e-04], [6.0956540e-05, 2.2225048e-04, 3.8728234e-04], [6.1642189e-05, 2.1964675e-04, 4.0369909e-04], [5.8844664e-05, 2.1374767e-04, 4.1417207e-04], [5.7150966e-05, 2.0920373e-04, 4.1820243e-04], [5.5285251e-05, 2.0723182e-04, 4.0379536e-04], [5.5278397e-05, 2.0315332e-04, 4.3007903e-04], [5.6005490e-05, 2.0021363e-04, 4.3119671e-04], [5.7905778e-05, 2.0268056e-04, 4.2984835e-04], [5.8969716e-05, 2.0108584e-04, 4.9466669e-04], [5.7197434e-05, 2.0400004e-04, 4.8310720e-04], [5.8316582e-05, 2.0677842e-04, 4.4798007e-04], [5.5217875e-05, 2.0273292e-04, 3.9979507e-04], [5.2473857e-05, 1.9077447e-04, 3.7621343e-04], [5.0210474e-05, 1.8508767e-04, 3.5905137e-04], [4.9514329e-05, 1.7809295e-04, 3.6660919e-04], [4.7522713e-05, 1.7627905e-04, 3.5958833e-04], [4.7432528e-05, 1.7114768e-04, 3.4196809e-04], [4.7061854e-05, 1.7858839e-04, 3.7684879e-04], ... [1.6946094e-04, 1.5064844e-04, 9.2533010e-05], [1.7211071e-04, 1.6139646e-04, 8.4166153e-05], [1.7541717e-04, 1.7660802e-04, 6.0221559e-05], [1.7440240e-04, 1.9544957e-04, 3.2458531e-05], [1.8071404e-04, 1.9552017e-04, 2.2991680e-05], [1.8980163e-04, 1.7540010e-04, 1.7840273e-05], [1.9475482e-04, 1.4813445e-04, 1.3792065e-05], [2.0412008e-04, 1.0925530e-04, 1.1346765e-05], [2.0858954e-04, 7.4563985e-05, 9.1552747e-06], [2.0937597e-04, 6.3926171e-05, 8.1477992e-06], [2.0614298e-04, 5.4879973e-05, 7.9349493e-06], [2.0774821e-04, 4.9834987e-05, 7.8745115e-06], [2.2729186e-04, 1.0725283e-04, 7.3192496e-06], [2.2412286e-04, 1.3586249e-04, 1.1071936e-05], [2.1546050e-04, 1.4760980e-04, 1.6061604e-05], [2.1067094e-04, 1.4497816e-04, 1.9443167e-05], [2.0641519e-04, 1.3815981e-04, 2.6482619e-05], [2.0400202e-04, 1.3395854e-04, 3.0892639e-05], [1.9902988e-04, 1.3089213e-04, 3.5797952e-05], [1.9289140e-04, 1.2865067e-04, 3.6081707e-05]]], dtype=float32) - N2(depth, hour, tau_bins)float320.0002396 0.0002716 ... 1.035e-05
- cell_measures :
- area: areacello
- cell_methods :
- area:mean zi:point yh:mean xh:mean time: mean
- long_name :
- Buoyancy frequency squared
- time_avg_info :
- average_T1,average_T2,average_DT
- units :
- s-2
array([[[2.39610745e-04, 2.71556084e-04, 2.70128832e-04], [2.40454014e-04, 2.70599645e-04, 2.74386490e-04], [2.38844208e-04, 2.72287260e-04, 2.75661034e-04], [2.39268178e-04, 2.72357342e-04, 2.73914455e-04], [2.38136767e-04, 2.70218472e-04, 2.74914317e-04], [2.40638648e-04, 2.65842449e-04, 2.78192281e-04], [2.39180474e-04, 2.64761271e-04, 2.83272122e-04], [2.38557084e-04, 2.67403608e-04, 2.80232227e-04], [2.40423135e-04, 2.67150201e-04, 2.80136708e-04], [2.40707173e-04, 2.69239477e-04, 2.72929028e-04], [2.40857858e-04, 2.67764030e-04, 2.78347288e-04], [2.40084250e-04, 2.69025011e-04, 2.78279331e-04], [2.41775560e-04, 2.68885138e-04, 2.73582235e-04], [2.38815803e-04, 2.68640055e-04, 2.75888189e-04], [2.39419605e-04, 2.66721123e-04, 2.76292703e-04], [2.39614528e-04, 2.65526905e-04, 2.78615538e-04], [2.39612215e-04, 2.63208203e-04, 2.79091299e-04], [2.38741486e-04, 2.62423680e-04, 2.80824257e-04], [2.38855631e-04, 2.61507288e-04, 2.79753702e-04], [2.38068678e-04, 2.64408678e-04, 2.79467757e-04], ... [4.28655549e-05, 3.03763100e-05, 1.97062764e-05], [4.30601140e-05, 3.16175210e-05, 1.90241481e-05], [4.39168252e-05, 3.30525654e-05, 1.80639163e-05], [4.37198396e-05, 3.46756715e-05, 1.31571032e-05], [4.35817747e-05, 3.43492829e-05, 9.33161937e-06], [4.41295670e-05, 3.12332850e-05, 7.67203892e-06], [4.47235834e-05, 2.71723238e-05, 6.19583034e-06], [4.54743567e-05, 2.19780250e-05, 5.31574733e-06], [4.57708011e-05, 1.73569697e-05, 4.65225321e-06], [4.60082410e-05, 1.53941182e-05, 4.28469411e-06], [4.54157052e-05, 1.37252200e-05, 4.09263794e-06], [4.43821191e-05, 1.29597693e-05, 4.18236550e-06], [4.52992681e-05, 1.17703157e-05, 3.95249845e-06], [4.71115054e-05, 1.77631937e-05, 3.62751052e-06], [4.60290321e-05, 2.39401288e-05, 4.93487551e-06], [4.58545837e-05, 2.56217081e-05, 5.63887988e-06], [4.60243464e-05, 2.62969115e-05, 7.22560526e-06], [4.58321811e-05, 2.66395800e-05, 8.58258500e-06], [4.60509145e-05, 2.69951252e-05, 9.79544802e-06], [4.51421438e-05, 2.74656868e-05, 1.03494922e-05]]], dtype=float32) - Rig(depth, hour, tau_bins)float323.263 1.133 ... 0.2238 0.2969
- cell_measures :
- area: areacello
- cell_methods :
- area:mean zi:point yh:mean xh:mean time: mean
- long_name :
- $Ri^g$
- time_avg_info :
- average_T1,average_T2,average_DT
array([[[3.2628639 , 1.132586 , 0.73984253], [3.2539244 , 1.1218415 , 0.6636562 ], [3.3077629 , 1.105064 , 0.657457 ], [3.199039 , 1.1237029 , 0.6516208 ], [3.407606 , 1.1350415 , 0.6307573 ], [3.551251 , 1.1491935 , 0.61930174], [3.7079988 , 1.1682833 , 0.6382069 ], [3.6843374 , 1.1969991 , 0.59546936], [3.6373034 , 1.1951932 , 0.58847064], [3.5286233 , 1.1990137 , 0.58950627], [3.5112283 , 1.2090486 , 0.5561185 ], [3.5832782 , 1.1928139 , 0.55407566], [3.6162767 , 1.197721 , 0.57535815], [3.6732585 , 1.2360073 , 0.6680939 ], [3.7434201 , 1.285768 , 0.6698258 ], [3.9210746 , 1.3179896 , 0.7200737 ], [4.1534557 , 1.3624804 , 0.72308815], [4.350609 , 1.3700106 , 0.72829914], [4.34737 , 1.4054053 , 0.74411196], [4.3841596 , 1.3435694 , 0.69021 ], ... [0.24822167, 0.2229091 , 0.22997776], [0.2502671 , 0.21606663, 0.2836377 ], [0.25094187, 0.20619413, 0.33462837], [0.25031978, 0.20203003, 0.39669967], [0.247252 , 0.20765834, 0.42320406], [0.24486798, 0.21742085, 0.43833 ], [0.2399987 , 0.22879753, 0.45103633], [0.2378625 , 0.24977483, 0.47837028], [0.23650162, 0.26490203, 0.50369376], [0.23604476, 0.27550876, 0.52109915], [0.2333923 , 0.27310133, 0.5113795 ], [0.23511393, 0.2857891 , 0.5116854 ], [0.19609238, 0.18043011, 0.53184634], [0.2021423 , 0.17112611, 0.33909 ], [0.20476255, 0.18330167, 0.29207003], [0.21014665, 0.19243236, 0.28591996], [0.21374545, 0.20258361, 0.2771893 ], [0.21875525, 0.21056795, 0.28172302], [0.2241939 , 0.21769346, 0.2848692 ], [0.23144257, 0.22380565, 0.29694226]]], dtype=float32) - Rig_T(depth, hour, tau_bins)float323.261 1.081 0.6602 ... 0.1893 0.283
- long_name :
- $Ri^g_T$
array([[[3.2613535 , 1.0807239 , 0.6601844 ], [3.2607088 , 1.0806876 , 0.6376095 ], [3.2541394 , 1.0787339 , 0.62403697], [3.1573467 , 1.095553 , 0.6195335 ], [3.33064 , 1.1045685 , 0.60020334], [3.4112148 , 1.1177173 , 0.57872057], [3.598929 , 1.1484542 , 0.59502447], [3.4866464 , 1.1638496 , 0.5739601 ], [3.478894 , 1.173312 , 0.5573975 ], [3.3537366 , 1.1519613 , 0.5667412 ], [3.2960985 , 1.1518707 , 0.5466292 ], [3.4085515 , 1.1364806 , 0.541849 ], [3.360477 , 1.1453516 , 0.56916815], [3.5060325 , 1.147816 , 0.64065945], [3.6237063 , 1.215729 , 0.6510779 ], [3.7974987 , 1.2563922 , 0.6879902 ], [4.037156 , 1.298629 , 0.6786159 ], [4.2002525 , 1.3074436 , 0.68557954], [4.2329836 , 1.3236547 , 0.71324885], [4.2080054 , 1.3151736 , 0.6461337 ], ... [0.19615808, 0.17713511, 0.17934614], [0.19791266, 0.16535255, 0.1786004 ], [0.19866161, 0.15758038, 0.18220627], [0.19679716, 0.14933537, 0.21518406], [0.19358617, 0.1441788 , 0.2393373 ], [0.18668334, 0.14489259, 0.255283 ], [0.18088251, 0.14877748, 0.27127427], [0.17411648, 0.15260799, 0.2903708 ], [0.16779342, 0.16068009, 0.30303138], [0.16678639, 0.16395637, 0.3173359 ], [0.16421464, 0.16863132, 0.3253452 ], [0.16048996, 0.17333059, 0.32974294], [0.15450153, 0.14349043, 0.34493858], [0.15779564, 0.14688559, 0.29000515], [0.1605247 , 0.1545775 , 0.25673643], [0.16559987, 0.16310802, 0.27112243], [0.17053735, 0.17055264, 0.2761741 ], [0.17479429, 0.1770179 , 0.27965385], [0.18131071, 0.18408577, 0.27241796], [0.18530713, 0.18929055, 0.2829873 ]]], dtype=float32) - tau(hour, tau_bins)float320.02975 0.05414 ... 0.05437 0.08353
- cell_methods :
- yh:mean xq:point time: mean
- interp_method :
- none
- long_name :
- Zonal surface stress from ocean interactions with atmos and ice
- time_avg_info :
- average_T1,average_T2,average_DT
- units :
- Pa
array([[0.02975217, 0.05414128, 0.08337677], [0.02997051, 0.05396286, 0.08334547], [0.02989443, 0.05379027, 0.08344196], [0.02965429, 0.05390463, 0.08429465], [0.02966079, 0.05406607, 0.08407371], [0.02973503, 0.05454572, 0.08423293], [0.02972214, 0.05489863, 0.08463559], [0.02960968, 0.05482671, 0.08451898], [0.02952631, 0.05462506, 0.08395986], [0.02949316, 0.05446932, 0.08439989], [0.02946503, 0.05439705, 0.08432356], [0.0292913 , 0.05438457, 0.08425272], [0.02905069, 0.05423319, 0.08445528], [0.02918139, 0.05503562, 0.08494085], [0.02949452, 0.0555568 , 0.08575647], [0.02982584, 0.05614248, 0.08632967], [0.03012207, 0.05678497, 0.08614163], [0.0303158 , 0.05744474, 0.08672108], [0.03049818, 0.05776399, 0.08675309], [0.03012105, 0.05725272, 0.08582553], [0.03001658, 0.05665065, 0.08521383], [0.03018327, 0.05611375, 0.08581671], [0.02999875, 0.05514082, 0.08425651], [0.02983666, 0.05437292, 0.08352954]], dtype=float32)
- title :
- KPP ν0=2.5, Ric=0.2, Ri0=0.5
<xarray.DatasetView> Dimensions: (depth: 6, hour: 24, tau_bins: 3) Coordinates: * depth (depth) float64 -89.0 -69.0 -59.0 -49.0 -39.0 -29.0 xh float64 -140.0 yh float64 0.0625 yq float64 -0.0625 * hour (hour) int64 0 1 2 3 4 5 6 7 8 9 ... 14 15 16 17 18 19 20 21 22 23 * tau_bins (tau_bins) object (0.0, 0.04] (0.04, 0.075] (0.075, inf] Data variables: KT (depth, hour, tau_bins) float32 1.001e-06 1.001e-06 ... 0.0001793 eps (depth, hour, tau_bins) float32 6.182e-10 1.664e-09 ... 1.489e-08 chi (depth, hour, tau_bins) float32 1.739e-08 2.265e-08 ... 2.921e-09 Jb (depth, hour, tau_bins) float32 2.555e-10 2.914e-10 ... 2.291e-11 Jq (depth, hour, tau_bins) float64 -0.4038 -0.4617 ... -30.73 -1.899 S2 (depth, hour, tau_bins) float32 6.214e-05 0.0002147 ... 3.608e-05 N2 (depth, hour, tau_bins) float32 0.0002396 0.0002716 ... 1.035e-05 Rig (depth, hour, tau_bins) float32 3.263 1.133 ... 0.2238 0.2969 Rig_T (depth, hour, tau_bins) float32 3.261 1.081 ... 0.1893 0.283 tau (hour, tau_bins) float32 0.02975 0.05414 ... 0.05437 0.08353 Attributes: title: KPP ν0=2.5, Ric=0.2, Ri0=0.5new_baseline.kpp.lmd.004- depth: 6
- hour: 24
- tau_bins: 3
- depth(depth)float64-89.0 -69.0 -59.0 -49.0 -39.0 -29.0
- axis :
- Z
- long_name :
- Interface pseudo-depth, -z*
- positive :
- up
- units :
- meter
array([-89., -69., -59., -49., -39., -29.])
- xh()float64-140.0
- axis :
- X
- domain_decomposition :
- [220, 222, 220, 221]
- long_name :
- h point nominal longitude
- units :
- degrees_east
array(-140.)
- yh()float640.0625
- axis :
- Y
- domain_decomposition :
- [210, 258, 210, 221]
- long_name :
- h point nominal latitude
- units :
- degrees_north
array(0.06249997)
- yq()float64-0.0625
- axis :
- Y
- domain_decomposition :
- [209, 257, 209, 221]
- long_name :
- q point nominal latitude
- units :
- degrees_north
array(-0.06249997)
- hour(hour)int640 1 2 3 4 5 6 ... 18 19 20 21 22 23
array([ 0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23]) - tau_bins(tau_bins)object(0.0, 0.04] ... (0.075, inf]
- cell_methods :
- yh:mean xq:point time: mean
- interp_method :
- none
- long_name :
- Zonal surface stress from ocean interactions with atmos and ice
- time_avg_info :
- average_T1,average_T2,average_DT
- units :
- Pa
array([Interval(0.0, 0.04, closed='right'), Interval(0.04, 0.075, closed='right'), Interval(0.075, inf, closed='right')], dtype=object)
- KT(depth, hour, tau_bins)float321.001e-06 1.001e-06 ... 1.001e-06
- cell_measures :
- area: areacello
- cell_methods :
- area:mean zi:point yh:mean xh:mean time: mean
- long_name :
- Total diapycnal diffusivity for heat at interfaces
- standard_name :
- ocean_vertical_heat_diffusivity
- time_avg_info :
- average_T1,average_T2,average_DT
- units :
- m2 s-1
array([[[1.00062630e-06, 1.00062630e-06, 1.00062607e-06], [1.00062630e-06, 1.00062630e-06, 1.00062596e-06], [1.00062630e-06, 1.00062630e-06, 1.00062596e-06], [1.00062630e-06, 1.00062618e-06, 1.00062596e-06], [1.00062630e-06, 1.00062618e-06, 1.00062607e-06], [1.00062630e-06, 1.00062630e-06, 1.00062607e-06], [1.00062630e-06, 1.00062630e-06, 1.00062607e-06], [1.00062630e-06, 1.00062630e-06, 1.00062607e-06], [1.00062630e-06, 1.00062630e-06, 1.00062618e-06], [1.00062630e-06, 1.00062630e-06, 1.00062618e-06], [1.00062630e-06, 1.00062630e-06, 1.00062630e-06], [1.00062630e-06, 1.00062630e-06, 1.00062618e-06], [1.00062630e-06, 1.00062630e-06, 1.00062618e-06], [1.00062630e-06, 1.00062630e-06, 1.00062618e-06], [1.00062630e-06, 1.00062630e-06, 1.00062607e-06], [1.00062630e-06, 1.00062618e-06, 1.00062618e-06], [1.00062630e-06, 1.00062630e-06, 1.00062607e-06], [1.00062630e-06, 1.00062630e-06, 1.00062618e-06], [1.00062630e-06, 1.00062618e-06, 1.00062618e-06], [1.00062630e-06, 1.00062618e-06, 1.00062618e-06], ... [3.81811027e-04, 2.90885218e-04, 1.71408691e-02], [4.25665407e-04, 6.40261802e-04, 4.36169878e-02], [4.42612451e-04, 1.11911795e-03, 5.60436398e-02], [4.89161233e-04, 5.52093564e-03, 7.04844594e-02], [5.36391861e-04, 1.31646777e-02, 7.70594999e-02], [6.37809746e-04, 2.09450833e-02, 8.12212825e-02], [7.49726663e-04, 2.86364648e-02, 8.68151113e-02], [9.01583699e-04, 3.39822620e-02, 8.97497833e-02], [1.04411470e-03, 3.73836383e-02, 9.08122957e-02], [1.13334751e-03, 3.80024388e-02, 8.79565999e-02], [1.30286883e-03, 4.09585051e-02, 8.61921236e-02], [1.38791348e-03, 4.12570350e-02, 8.57141986e-02], [8.68165516e-04, 1.87469891e-03, 2.38270964e-02], [7.35677429e-04, 2.46586249e-04, 1.61692663e-03], [8.00757844e-04, 9.72932903e-05, 2.39668181e-03], [7.69015285e-04, 3.98732591e-05, 6.91609748e-04], [7.04221777e-04, 2.61289642e-05, 3.45671833e-05], [6.36181561e-04, 8.17534328e-06, 1.58894795e-06], [5.70160919e-04, 3.65733058e-06, 1.00068951e-06], [5.23375580e-04, 1.35793562e-06, 1.00068837e-06]]], dtype=float32) - eps(depth, hour, tau_bins)float321.181e-09 7.053e-08 ... 1.901e-09
- long_name :
- $ε$
- units :
- W/kg
array([[[1.18064569e-09, 7.05251040e-08, 2.77265144e-07], [1.17697518e-09, 6.97652922e-08, 2.77346743e-07], [1.23723454e-09, 6.87601371e-08, 2.65645610e-07], [1.18744836e-09, 7.00886318e-08, 2.49900353e-07], [1.13138343e-09, 6.80724241e-08, 2.20856890e-07], [1.05763143e-09, 5.98049397e-08, 2.27534343e-07], [1.03238118e-09, 5.61502809e-08, 1.94415634e-07], [1.02243036e-09, 5.38770273e-08, 1.87493413e-07], [1.03555686e-09, 5.08423632e-08, 1.82726680e-07], [1.12074439e-09, 4.81827485e-08, 1.85923653e-07], [1.18264931e-09, 4.74369664e-08, 1.98140725e-07], [1.21930477e-09, 4.99082411e-08, 1.98121114e-07], [1.21355803e-09, 5.19479499e-08, 1.89243195e-07], [1.17654253e-09, 4.82414180e-08, 1.89402016e-07], [1.13012932e-09, 4.11239540e-08, 1.79092623e-07], [1.09471476e-09, 3.74310609e-08, 1.71340531e-07], [1.06548481e-09, 3.41670372e-08, 2.20125287e-07], [1.02419428e-09, 3.14334869e-08, 2.32289850e-07], [9.57743884e-10, 2.92911473e-08, 2.34592022e-07], [9.83565784e-10, 3.88315229e-08, 2.46494466e-07], ... [7.89389105e-08, 8.09131961e-08, 6.12292922e-07], [8.69531078e-08, 2.28870036e-07, 7.79314803e-07], [9.55727728e-08, 4.57868623e-07, 7.80822688e-07], [1.06504153e-07, 6.26281007e-07, 6.89955129e-07], [1.33650801e-07, 6.77400521e-07, 5.93021298e-07], [1.84921817e-07, 6.60766887e-07, 4.93917923e-07], [2.31475937e-07, 6.05025548e-07, 4.17285662e-07], [2.99835392e-07, 5.35225240e-07, 3.60747038e-07], [3.61126069e-07, 4.55689417e-07, 3.15176209e-07], [3.34447122e-07, 4.01052773e-07, 2.99641272e-07], [3.45345427e-07, 3.78920788e-07, 2.82839551e-07], [3.29040120e-07, 3.50103562e-07, 2.86943362e-07], [1.45253949e-07, 6.14728890e-08, 9.53904760e-08], [1.38159805e-07, 1.47678936e-08, 1.28765443e-08], [1.49180920e-07, 1.29572850e-08, 1.89057943e-08], [1.46155969e-07, 1.01477031e-08, 1.00389563e-08], [1.36401056e-07, 8.72085781e-09, 3.28854233e-09], [1.22148748e-07, 7.87655008e-09, 1.59576929e-09], [1.08910697e-07, 7.31133554e-09, 1.24768662e-09], [9.78104424e-08, 7.43829576e-09, 1.90076577e-09]]], dtype=float32) - chi(depth, hour, tau_bins)float322.421e-08 4.218e-08 ... 1.773e-11
- long_name :
- $χ$
- units :
- C^2/s
array([[[2.42052565e-08, 4.21824211e-08, 6.13486506e-08], [2.43459812e-08, 4.24095639e-08, 6.18638438e-08], [2.49258587e-08, 4.28722551e-08, 6.38544719e-08], [2.41345166e-08, 4.36538734e-08, 6.12625897e-08], [2.41304896e-08, 4.32199982e-08, 6.58363177e-08], [2.39560300e-08, 4.23458459e-08, 6.48738592e-08], [2.39092675e-08, 4.18693453e-08, 6.48294289e-08], [2.36714595e-08, 4.21219220e-08, 6.36250235e-08], [2.35294735e-08, 4.19933741e-08, 6.35801527e-08], [2.38001778e-08, 4.14795842e-08, 6.10450712e-08], [2.42841285e-08, 4.09588949e-08, 6.14152711e-08], [2.45673526e-08, 4.12250003e-08, 6.06932744e-08], [2.50661039e-08, 4.04394562e-08, 6.33500861e-08], [2.45900935e-08, 4.08110026e-08, 6.14272153e-08], [2.40473650e-08, 3.92472401e-08, 6.08052844e-08], [2.33138522e-08, 3.92737824e-08, 5.67299274e-08], [2.31825688e-08, 3.83459664e-08, 5.78424597e-08], [2.29814709e-08, 3.78962568e-08, 5.81310964e-08], [2.24128005e-08, 3.67799586e-08, 5.87707021e-08], [2.26952821e-08, 3.83477641e-08, 5.96626819e-08], ... [6.58048833e-08, 3.45897888e-08, 3.06971515e-07], [7.74475311e-08, 1.30058410e-07, 4.16388218e-07], [8.59298765e-08, 2.74952754e-07, 3.99758875e-07], [9.98236942e-08, 3.31715938e-07, 2.91729918e-07], [1.24177078e-07, 3.48626457e-07, 2.22114693e-07], [1.59281512e-07, 3.29062061e-07, 1.67315164e-07], [1.86461662e-07, 2.82267621e-07, 1.27866073e-07], [2.02538843e-07, 2.26290396e-07, 9.58634558e-08], [2.10553225e-07, 1.79261093e-07, 8.23487909e-08], [1.90766357e-07, 1.44952992e-07, 7.88916452e-08], [1.84443806e-07, 1.37191520e-07, 7.31033865e-08], [1.62389298e-07, 1.18587856e-07, 7.63602017e-08], [8.45517505e-08, 9.69671721e-09, 2.11790869e-08], [7.38182564e-08, 1.38504741e-09, 1.56564184e-09], [1.16998599e-07, 9.71107417e-10, 3.63399155e-09], [1.27962963e-07, 5.68661396e-10, 2.04201012e-09], [1.21315836e-07, 4.69920158e-10, 1.60213856e-10], [1.14172195e-07, 1.94918595e-10, 2.22373005e-11], [1.04255875e-07, 1.51198110e-10, 1.14214176e-11], [9.37985618e-08, 1.06092787e-10, 1.77340642e-11]]], dtype=float32) - Jb(depth, hour, tau_bins)float323.165e-10 4.063e-10 ... 5.549e-12
- cell_measures :
- area: areacello
- cell_methods :
- area:mean zi:point yh:mean xh:mean time: mean
- long_name :
- Total diapycnal diffusivity for heat at interfaces
- standard_name :
- ocean_vertical_diffusive_buoyancy_flux
- time_avg_info :
- average_T1,average_T2,average_DT
- units :
- m2 s-1
array([[[3.16469878e-10, 4.06335215e-10, 4.95005370e-10], [3.17809973e-10, 4.08817480e-10, 5.05972153e-10], [3.19277160e-10, 4.06790518e-10, 5.20859522e-10], [3.16120546e-10, 4.13883317e-10, 5.00933961e-10], [3.13139681e-10, 4.10488865e-10, 5.20764376e-10], [3.10633408e-10, 4.08861972e-10, 5.17567100e-10], [3.07887188e-10, 4.05906087e-10, 5.04892905e-10], [3.05545256e-10, 4.04478839e-10, 5.07665687e-10], [3.04483549e-10, 4.05523504e-10, 5.02378528e-10], [3.06875358e-10, 4.05313921e-10, 4.88781682e-10], [3.09053727e-10, 4.06532308e-10, 4.90021246e-10], [3.14069798e-10, 4.04227846e-10, 4.88690088e-10], [3.19417853e-10, 4.00812328e-10, 4.95343877e-10], [3.16587395e-10, 4.00275202e-10, 4.89477736e-10], [3.14177545e-10, 3.95527555e-10, 4.95618879e-10], [3.08587988e-10, 3.92145816e-10, 4.73352690e-10], [3.05383857e-10, 3.87099630e-10, 4.77537287e-10], [3.04662073e-10, 3.82146120e-10, 4.79010498e-10], [3.03895215e-10, 3.79181186e-10, 4.81312434e-10], [3.04242853e-10, 3.84696941e-10, 4.91413632e-10], ... [3.82668297e-09, 2.23462582e-09, 1.09350111e-07], [5.13596010e-09, 1.12433076e-08, 1.73453998e-07], [6.16254914e-09, 2.75167942e-08, 1.78586021e-07], [7.04082215e-09, 5.64420901e-08, 1.63006959e-07], [9.40332612e-09, 8.36426182e-08, 1.39608161e-07], [1.21739756e-08, 9.57661683e-08, 1.16583863e-07], [1.44949128e-08, 9.81782833e-08, 1.04734198e-07], [1.82505584e-08, 9.62569473e-08, 9.39466602e-08], [2.09147721e-08, 8.83932714e-08, 8.06017368e-08], [2.07831228e-08, 7.65039445e-08, 8.95265799e-08], [2.29593340e-08, 7.25662659e-08, 7.88470302e-08], [2.28492016e-08, 6.55638672e-08, 8.51448192e-08], [7.32230188e-09, 3.86669319e-09, 2.06859028e-08], [5.07420683e-09, 1.86237886e-10, 6.27351116e-10], [8.44772785e-09, 7.74282166e-11, 2.03356487e-09], [9.28062427e-09, 1.02767976e-11, 6.27712660e-10], [8.80577300e-09, 6.66913183e-12, 3.52873321e-11], [8.48780868e-09, 5.79612356e-12, 6.17024704e-12], [7.54977414e-09, 5.89862187e-12, 4.79358827e-12], [6.58689014e-09, 6.02329558e-12, 5.54850436e-12]]], dtype=float32) - Jq(depth, hour, tau_bins)float64-0.4816 -0.6392 ... -0.01328
- units :
- W/m^2
- long_name :
- $J_q^t$
array([[[-4.81552918e-01, -6.39167771e-01, -7.62933746e-01], [-4.85329101e-01, -6.42421775e-01, -7.69579102e-01], [-4.90946456e-01, -6.40105755e-01, -7.79354780e-01], [-4.83578128e-01, -6.47227242e-01, -7.73838051e-01], [-4.83167888e-01, -6.42581872e-01, -7.91665006e-01], [-4.80104343e-01, -6.40745775e-01, -7.90478163e-01], [-4.77434282e-01, -6.38051315e-01, -7.81818421e-01], [-4.75500770e-01, -6.35332332e-01, -7.90931913e-01], [-4.73519131e-01, -6.37496725e-01, -7.95123686e-01], [-4.78122582e-01, -6.36972344e-01, -7.80516887e-01], [-4.82024922e-01, -6.37689752e-01, -7.90518737e-01], [-4.84282250e-01, -6.35676375e-01, -7.79288919e-01], [-4.88477602e-01, -6.29759740e-01, -7.87195326e-01], [-4.83797879e-01, -6.26795017e-01, -7.73909660e-01], [-4.78714276e-01, -6.17771603e-01, -7.74951022e-01], [-4.71088650e-01, -6.12925103e-01, -7.50714301e-01], [-4.70555356e-01, -6.07242223e-01, -7.45696696e-01], [-4.70414981e-01, -6.04348376e-01, -7.45931460e-01], [-4.67814145e-01, -5.97328799e-01, -7.44359541e-01], [-4.64213041e-01, -6.07979658e-01, -7.65086306e-01], ... [-1.52504881e+01, -1.08198466e+01, -2.40912335e+02], [-1.79780728e+01, -2.77116033e+01, -4.22594618e+02], [-1.87859441e+01, -6.95042618e+01, -4.35070729e+02], [-2.05550840e+01, -1.68302990e+02, -4.19219828e+02], [-2.43782413e+01, -2.28539469e+02, -3.79352246e+02], [-2.99324167e+01, -2.53416225e+02, -3.39421920e+02], [-3.62329256e+01, -2.54032476e+02, -3.03281977e+02], [-4.57524427e+01, -2.37408954e+02, -2.80372229e+02], [-5.52564399e+01, -2.23234615e+02, -2.56960106e+02], [-5.82335571e+01, -2.00110950e+02, -2.44827332e+02], [-6.62942474e+01, -1.96551320e+02, -2.38200016e+02], [-7.06634387e+01, -1.84589225e+02, -2.36158794e+02], [-2.57588716e+01, -1.98516862e+01, -7.16579682e+01], [-2.11297949e+01, -2.07265483e+00, -6.26872271e+00], [-2.92281227e+01, -1.13948511e+00, -1.02573476e+01], [-2.99932999e+01, -5.12616250e-01, -4.02535216e+00], [-2.89512937e+01, -3.15996434e-01, -2.26048104e-01], [-2.69464025e+01, -1.24594175e-01, -1.93938576e-02], [-2.45196400e+01, -7.30878555e-02, -1.04230675e-02], [-2.18747312e+01, -4.18680368e-02, -1.32777846e-02]]]) - S2(depth, hour, tau_bins)float320.0001418 0.0004672 ... 1.13e-05
- long_name :
- $S^2$
- units :
- s$^{-2}$
array([[[1.41836601e-04, 4.67205304e-04, 7.15640199e-04], [1.41885423e-04, 4.72498999e-04, 7.31437409e-04], [1.48016523e-04, 4.70162660e-04, 7.41417753e-04], [1.40774020e-04, 4.72693850e-04, 7.54935376e-04], [1.37023657e-04, 4.75256471e-04, 6.97829120e-04], [1.28246960e-04, 4.59599047e-04, 7.07112486e-04], [1.25261853e-04, 4.47610510e-04, 6.60455436e-04], [1.26983170e-04, 4.52060514e-04, 6.61135535e-04], [1.30664936e-04, 4.44407866e-04, 6.85080537e-04], [1.37154537e-04, 4.38653689e-04, 6.94098591e-04], [1.42129575e-04, 4.40310774e-04, 7.55701447e-04], [1.44565871e-04, 4.36307222e-04, 7.79487425e-04], [1.45691098e-04, 4.47019353e-04, 7.87896803e-04], [1.44280799e-04, 4.34455666e-04, 7.75266206e-04], [1.38319811e-04, 4.21505451e-04, 7.35988317e-04], [1.30991262e-04, 4.10690991e-04, 7.31264823e-04], [1.23740640e-04, 3.95052601e-04, 7.36095477e-04], [1.19035845e-04, 3.91300389e-04, 7.24209938e-04], [1.12783542e-04, 3.89472814e-04, 7.19981967e-04], [1.15830204e-04, 4.13761125e-04, 7.08727632e-04], ... [1.48438034e-04, 8.40546709e-05, 3.76138487e-05], [1.49744868e-04, 8.34067832e-05, 2.62681206e-05], [1.55048183e-04, 8.11126229e-05, 1.71083029e-05], [1.57887640e-04, 7.14546768e-05, 1.13211072e-05], [1.63779288e-04, 5.51105259e-05, 8.39280438e-06], [1.68770872e-04, 4.05372193e-05, 6.78651486e-06], [1.68515748e-04, 2.62506983e-05, 5.61330125e-06], [1.69917534e-04, 1.88715403e-05, 4.70684881e-06], [1.62872253e-04, 1.40121865e-05, 4.11091787e-06], [1.63078847e-04, 1.16760530e-05, 3.64465927e-06], [1.55001995e-04, 1.02589747e-05, 3.68917995e-06], [1.50725929e-04, 9.34424224e-06, 3.74268780e-06], [1.62076351e-04, 1.31826091e-05, 3.24889447e-06], [1.72409025e-04, 1.79971466e-05, 4.08658934e-06], [1.71353196e-04, 2.51919682e-05, 4.80949393e-06], [1.65656005e-04, 3.08949420e-05, 5.40391466e-06], [1.60071344e-04, 3.37284146e-05, 6.34383969e-06], [1.54592650e-04, 3.72321556e-05, 7.46843898e-06], [1.50900625e-04, 4.07821572e-05, 8.78149694e-06], [1.46861552e-04, 4.46950471e-05, 1.12951284e-05]]], dtype=float32) - N2(depth, hour, tau_bins)float320.0002504 0.0002843 ... 7.772e-06
- cell_measures :
- area: areacello
- cell_methods :
- area:mean zi:point yh:mean xh:mean time: mean
- long_name :
- Buoyancy frequency squared
- time_avg_info :
- average_T1,average_T2,average_DT
- units :
- s-2
array([[[2.50380457e-04, 2.84322537e-04, 3.26497160e-04], [2.49794335e-04, 2.84366863e-04, 3.40093946e-04], [2.50732730e-04, 2.82385619e-04, 3.52266827e-04], [2.48224998e-04, 2.84529960e-04, 3.56787234e-04], [2.50212091e-04, 2.83362053e-04, 3.48632922e-04], [2.50026409e-04, 2.83005007e-04, 3.47889756e-04], [2.52470782e-04, 2.82140682e-04, 3.33684904e-04], [2.52225524e-04, 2.83426692e-04, 3.28114023e-04], [2.52644822e-04, 2.83705245e-04, 3.10908479e-04], [2.55907566e-04, 2.84160778e-04, 2.99137144e-04], [2.56487081e-04, 2.83852918e-04, 2.97512830e-04], [2.59825640e-04, 2.79677886e-04, 3.08105693e-04], [2.58581596e-04, 2.79740925e-04, 3.08726507e-04], [2.58143933e-04, 2.80908833e-04, 3.07591661e-04], [2.53710255e-04, 2.79614876e-04, 3.14174657e-04], [2.53522012e-04, 2.79249914e-04, 3.10601346e-04], [2.52498110e-04, 2.75294151e-04, 3.15695826e-04], [2.51230667e-04, 2.75489874e-04, 3.10448901e-04], [2.47999094e-04, 2.76833016e-04, 3.11023148e-04], [2.47006770e-04, 2.79002707e-04, 3.11012758e-04], ... [4.00318095e-05, 2.19094200e-05, 1.75809691e-05], [4.08545384e-05, 2.29993839e-05, 1.44682808e-05], [4.17602168e-05, 2.32393431e-05, 1.14376789e-05], [4.19672360e-05, 2.17431643e-05, 7.65365985e-06], [4.25652397e-05, 1.95154098e-05, 5.98406086e-06], [4.24695463e-05, 1.64385110e-05, 5.06878587e-06], [4.20580618e-05, 1.29026885e-05, 4.27718123e-06], [4.17472147e-05, 9.78873140e-06, 3.76438538e-06], [4.05107130e-05, 7.85583507e-06, 3.40888255e-06], [3.98970660e-05, 6.68765961e-06, 3.17039030e-06], [3.72795621e-05, 6.00186786e-06, 3.05350545e-06], [3.63023901e-05, 5.69715758e-06, 3.10032669e-06], [3.58221514e-05, 5.26593840e-06, 2.91269725e-06], [3.86316751e-05, 5.47839863e-06, 2.88081537e-06], [4.08812521e-05, 7.10814311e-06, 3.56441478e-06], [4.26108927e-05, 8.30356112e-06, 4.09821496e-06], [4.13776215e-05, 9.73325223e-06, 4.94596861e-06], [4.03243102e-05, 1.09723651e-05, 5.75752665e-06], [4.00129029e-05, 1.23583932e-05, 6.63946867e-06], [3.99043274e-05, 1.35408591e-05, 7.77176410e-06]]], dtype=float32) - Rig(depth, hour, tau_bins)float321.384 0.4356 ... 0.3581 0.6224
- cell_measures :
- area: areacello
- cell_methods :
- area:mean zi:point yh:mean xh:mean time: mean
- long_name :
- $Ri^g$
- time_avg_info :
- average_T1,average_T2,average_DT
array([[[1.3843784 , 0.4355776 , 0.3091246 ], [1.3862953 , 0.42921245, 0.29979953], [1.3186188 , 0.43328127, 0.29791135], [1.319762 , 0.431867 , 0.31552285], [1.3876023 , 0.42956513, 0.33238146], [1.4713855 , 0.43854415, 0.33611113], [1.4585589 , 0.44340074, 0.3397169 ], [1.422827 , 0.44158596, 0.33740765], [1.4308827 , 0.44296315, 0.3283518 ], [1.3624867 , 0.44709092, 0.32959506], [1.3321421 , 0.44995126, 0.31895676], [1.3095046 , 0.44489077, 0.31756058], [1.299477 , 0.44661266, 0.31964993], [1.3353751 , 0.45561314, 0.32711515], [1.3878534 , 0.46209648, 0.33242503], [1.4295163 , 0.47146723, 0.3417432 ], [1.5292119 , 0.48259148, 0.33962864], [1.5888655 , 0.49773607, 0.3420213 ], [1.6786402 , 0.50815296, 0.34517553], [1.6653159 , 0.49143514, 0.32707235], ... [0.28946295, 0.31139803, 0.48474592], [0.28830624, 0.31103155, 0.6091647 ], [0.28619725, 0.31105167, 0.6668595 ], [0.27883843, 0.3508357 , 0.70064497], [0.27638355, 0.40184402, 0.73615754], [0.2694257 , 0.4452605 , 0.747144 ], [0.2700301 , 0.5024443 , 0.76513684], [0.27028793, 0.53571266, 0.8011063 ], [0.27429098, 0.5733974 , 0.8354088 ], [0.27542618, 0.5895082 , 0.85250646], [0.27886292, 0.59905994, 0.8383981 ], [0.27949739, 0.61504954, 0.85223484], [0.23919442, 0.4293044 , 0.8984655 ], [0.23977003, 0.3274899 , 0.7073171 ], [0.24828433, 0.31492984, 0.70929885], [0.2581412 , 0.31994987, 0.7032059 ], [0.2622922 , 0.32738152, 0.69430584], [0.26598185, 0.33688056, 0.6817956 ], [0.27302545, 0.34765816, 0.68264323], [0.28132206, 0.35813454, 0.6223506 ]]], dtype=float32) - Rig_T(depth, hour, tau_bins)float321.343 0.4258 0.304 ... 0.297 0.5564
- long_name :
- $Ri^g_T$
array([[[1.3434284 , 0.42575198, 0.30399483], [1.339348 , 0.42032528, 0.2998174 ], [1.2688062 , 0.42568558, 0.3012114 ], [1.2518506 , 0.42318174, 0.3039454 ], [1.3326304 , 0.42496133, 0.3102635 ], [1.4218832 , 0.43632212, 0.31440943], [1.384247 , 0.44214174, 0.33351892], [1.3839062 , 0.4455327 , 0.32989636], [1.3978841 , 0.4521952 , 0.32683736], [1.3210678 , 0.4536286 , 0.31877822], [1.2479538 , 0.4600122 , 0.3171023 ], [1.2329819 , 0.45810977, 0.31853944], [1.244715 , 0.45624778, 0.31939033], [1.2971063 , 0.46135846, 0.32418156], [1.3399007 , 0.46895933, 0.33118114], [1.3868494 , 0.47377062, 0.34303665], [1.536852 , 0.4890711 , 0.33796883], [1.5975964 , 0.49740615, 0.33952767], [1.6472256 , 0.50301504, 0.33876863], [1.6406693 , 0.49364513, 0.3232821 ], ... [0.21892303, 0.24460581, 0.2782022 ], [0.21715575, 0.23441526, 0.3032993 ], [0.21339254, 0.22937962, 0.33986917], [0.20731284, 0.22571316, 0.3779726 ], [0.20430309, 0.23617734, 0.4060605 ], [0.19837219, 0.24865082, 0.4239027 ], [0.19388697, 0.27108073, 0.44667554], [0.19081205, 0.2972318 , 0.46966553], [0.18402362, 0.32012552, 0.47574738], [0.18582553, 0.3224433 , 0.5086578 ], [0.18361391, 0.34084806, 0.52148837], [0.18255128, 0.34092087, 0.52430046], [0.17780563, 0.27581286, 0.59936225], [0.18213266, 0.26158136, 0.53423965], [0.18586144, 0.25454444, 0.54415107], [0.19341025, 0.26411104, 0.5709722 ], [0.19832414, 0.2755742 , 0.5958633 ], [0.20684622, 0.28239402, 0.59805214], [0.21064335, 0.29060137, 0.5932599 ], [0.2162599 , 0.29699275, 0.5563707 ]]], dtype=float32) - tau(hour, tau_bins)float320.02927 0.0545 ... 0.05481 0.08379
- cell_methods :
- yh:mean xq:point time: mean
- interp_method :
- none
- long_name :
- Zonal surface stress from ocean interactions with atmos and ice
- time_avg_info :
- average_T1,average_T2,average_DT
- units :
- Pa
array([[0.02926666, 0.05449591, 0.08384066], [0.02953365, 0.05412898, 0.08342438], [0.02950766, 0.0540043 , 0.08344106], [0.02929587, 0.05410497, 0.08466474], [0.0292785 , 0.05410444, 0.08424635], [0.02926119, 0.05435768, 0.08391857], [0.02946588, 0.05500093, 0.08486678], [0.02949753, 0.05470305, 0.08457853], [0.02931711, 0.05448961, 0.08386523], [0.02923332, 0.05441001, 0.08456027], [0.02923119, 0.05438141, 0.08475135], [0.02906845, 0.05451858, 0.0846531 ], [0.02867212, 0.05450471, 0.08504945], [0.02912105, 0.05511454, 0.0853009 ], [0.02948293, 0.05570985, 0.08595277], [0.0298175 , 0.05617661, 0.08633688], [0.030358 , 0.0568147 , 0.08630857], [0.0307796 , 0.05742799, 0.08671759], [0.03049245, 0.05769216, 0.08660597], [0.0303795 , 0.05715569, 0.08534982], [0.03001719, 0.05657351, 0.08543649], [0.03012108, 0.05630453, 0.08567342], [0.02991865, 0.05550613, 0.08454789], [0.02963275, 0.05481243, 0.08379018]], dtype=float32)
- title :
- KPP ν0=2.5, Ri0=0.5
<xarray.DatasetView> Dimensions: (depth: 6, hour: 24, tau_bins: 3) Coordinates: * depth (depth) float64 -89.0 -69.0 -59.0 -49.0 -39.0 -29.0 xh float64 -140.0 yh float64 0.0625 yq float64 -0.0625 * hour (hour) int64 0 1 2 3 4 5 6 7 8 9 ... 14 15 16 17 18 19 20 21 22 23 * tau_bins (tau_bins) object (0.0, 0.04] (0.04, 0.075] (0.075, inf] Data variables: KT (depth, hour, tau_bins) float32 1.001e-06 1.001e-06 ... 1.001e-06 eps (depth, hour, tau_bins) float32 1.181e-09 7.053e-08 ... 1.901e-09 chi (depth, hour, tau_bins) float32 2.421e-08 4.218e-08 ... 1.773e-11 Jb (depth, hour, tau_bins) float32 3.165e-10 4.063e-10 ... 5.549e-12 Jq (depth, hour, tau_bins) float64 -0.4816 -0.6392 ... -0.01328 S2 (depth, hour, tau_bins) float32 0.0001418 0.0004672 ... 1.13e-05 N2 (depth, hour, tau_bins) float32 0.0002504 0.0002843 ... 7.772e-06 Rig (depth, hour, tau_bins) float32 1.384 0.4356 ... 0.3581 0.6224 Rig_T (depth, hour, tau_bins) float32 1.343 0.4258 ... 0.297 0.5564 tau (hour, tau_bins) float32 0.02927 0.0545 ... 0.05481 0.08379 Attributes: title: KPP ν0=2.5, Ri0=0.5new_baseline.kpp.lmd.005
euc relative frame#
# could be cleaned up
newtree = mixpods.bin_to_euc_centered_coordinate(tree)
# merge tree
for nodename, _ in tree.children.items():
tree[f"{nodename}/euc"] = newtree[f"{nodename}/euc"]
euc_mean = mixpods.average_euc(newtree)
for nodename, _ in tree.children.items():
tree[f"{nodename}/euc/mean"] = euc_mean[f"{nodename}"]
euc_subset = DataTree()
for nodename, _ in tree.children.items():
euc_subset[f"{nodename}"] = tree[f"{nodename}/euc"]
euc_subset.to_zarr("mom6-euc-coordinate.zarr")
<__array_function__ internals>:200: RuntimeWarning: invalid value encountered in cast
<__array_function__ internals>:200: RuntimeWarning: invalid value encountered in cast
<__array_function__ internals>:200: RuntimeWarning: invalid value encountered in cast
<__array_function__ internals>:200: RuntimeWarning: invalid value encountered in cast
<__array_function__ internals>:200: RuntimeWarning: invalid value encountered in cast
<__array_function__ internals>:200: RuntimeWarning: invalid value encountered in cast
<__array_function__ internals>:200: RuntimeWarning: invalid value encountered in cast
euc = datatree.open_datatree("mom6-euc-coordinate.zarr", engine="zarr")
euc_mean = euc.dc.extract_leaf("mean")
tree = tree.dc.insert_as_subtree("euc", euc)
Merge in microstructure#
micro_zeuc = datatree.open_datatree(
os.path.expanduser("~/datasets/microstructure/equix-tiwe-zeuc.average.nc")
).load()
for nodename, node in micro_zeuc.children.items():
node["u"].attrs["standard_name"] = "sea_water_x_velocity"
node["v"].attrs["standard_name"] = "sea_water_y_velocity"
node["KT"].attrs["standard_name"] = "ocean_vertical_heat_diffusivity"
node["ν"].attrs["standard_name"] = "ocean_vertical_momentum_diffusivity"
del node["Rig_T"].attrs["standard_name"]
del node["N2T"].attrs["standard_name"]
# euc_mean[nodename] = node
for nodename, node in micro_zeuc.children.items():
euc_mean[nodename] = node
Validate before continuing#
mixpods.validate_tree(tree)
Persist tree#
tree = mixpods.persist_tree(tree)
euc_mean.load()
<xarray.DatasetView>
Dimensions: ()
Data variables:
*empty*- zeuc: 50
- latitude()float320.0
array(0., dtype=float32)
- longitude()float32-140.0
array(-140., dtype=float32)
- reference_pressure()int640
array(0)
- zeuc(zeuc)float64-297.5 -287.5 ... 182.5 192.5
array([-297.5, -287.5, -277.5, -267.5, -257.5, -247.5, -237.5, -227.5, -217.5, -207.5, -197.5, -187.5, -177.5, -167.5, -157.5, -147.5, -137.5, -127.5, -117.5, -107.5, -97.5, -87.5, -77.5, -67.5, -57.5, -47.5, -37.5, -27.5, -17.5, -7.5, 2.5, 12.5, 22.5, 32.5, 42.5, 52.5, 62.5, 72.5, 82.5, 92.5, 102.5, 112.5, 122.5, 132.5, 142.5, 152.5, 162.5, 172.5, 182.5, 192.5])
- u(zeuc)float32nan nan nan ... -0.2604 -0.3176 nan
- FORTRAN_format :
- epic_code :
- 1205
- generic_name :
- u
- long_name :
- u
- name :
- u
- standard_name :
- sea_water_x_velocity
- units :
- m/s
array([ nan, nan, nan, -0.34412488, -0.18681769, -0.13121113, -0.13110007, -0.13295907, -0.11932758, -0.08944993, -0.06136366, -0.02866847, 0.00694463, 0.04042118, 0.0743546 , 0.10901111, 0.14541332, 0.18769473, 0.23589374, 0.2907243 , 0.3542546 , 0.42696685, 0.50931793, 0.6012157 , 0.7011007 , 0.8074448 , 0.91773796, 1.0287796 , 1.1347147 , 1.2393551 , 1.2138937 , 1.0788711 , 0.91834927, 0.70995677, 0.5181305 , 0.3438547 , 0.18748318, 0.05523353, -0.04432773, -0.11947555, -0.17376517, -0.21139875, -0.24530154, -0.270233 , -0.24724326, -0.17648801, -0.07476283, -0.26038134, -0.31755555, nan], dtype=float32) - v(zeuc)float32nan nan nan ... 0.02461 nan
- FORTRAN_format :
- epic_code :
- 1206
- generic_name :
- v
- long_name :
- v
- name :
- v
- standard_name :
- sea_water_y_velocity
- units :
- m/s
array([ nan, nan, nan, 0.04195916, 0.05380342, 0.01274851, 0.00227688, -0.00924548, -0.01995938, -0.01586557, -0.01145717, -0.00865888, -0.00767306, -0.00643414, -0.00592971, -0.004652 , -0.00347026, -0.00338077, -0.00492731, -0.00728909, -0.00941122, -0.01004394, -0.00936652, -0.00851536, -0.00894195, -0.01160023, -0.01552894, -0.01913672, -0.02083625, -0.02033128, -0.01806714, -0.02482096, -0.03544367, -0.03519909, -0.03364132, -0.03147318, -0.030247 , -0.02728803, -0.02601177, -0.02645807, -0.02617701, -0.02455281, -0.02730868, -0.0510966 , -0.04896826, -0.01222637, -0.04429276, -0.02813384, 0.02461111, nan], dtype=float32) - theta(zeuc)float64nan 11.76 11.79 ... 27.16 27.48
- description :
- potential temperature using T, S=35
- long_name :
- $θ$
- standard_name :
- sea_water_potential_temperature
- units :
- degC
array([ nan, 11.75736668, 11.79488486, 11.84763801, 11.93816873, 12.05439489, 12.13125832, 12.06222168, 11.96635654, 11.9495776 , 11.98335018, 12.04190823, 12.1266774 , 12.23353507, 12.36732257, 12.53790349, 12.74391642, 12.98199528, 13.24475614, 13.53098288, 13.84172953, 14.17438081, 14.55655615, 15.0141248 , 15.57278846, 16.24306478, 17.04445091, 17.97797458, 19.09056328, 20.27529103, 21.49486726, 22.6751356 , 23.67650756, 24.39508511, 24.92568977, 25.29087219, 25.54098213, 25.70651579, 25.81024759, 25.87502808, 25.94671359, 26.06974854, 26.271824 , 26.51445195, 26.76821484, 26.96498047, 27.0757443 , 27.26711222, 27.1600157 , 27.47678427]) - S2(zeuc)float32nan nan nan ... 3.295e-05 nan
- long_name :
- $S^2$
array([ nan, nan, nan, 4.00262506e-05, 1.99538299e-05, 2.05517135e-05, 2.50414141e-05, 2.88889587e-05, 2.94069741e-05, 2.84150774e-05, 2.68097574e-05, 2.58364780e-05, 2.66908228e-05, 2.83490008e-05, 3.06443253e-05, 3.34476608e-05, 3.77655488e-05, 4.43660574e-05, 5.27910342e-05, 6.30350623e-05, 7.58075330e-05, 9.16752906e-05, 1.10956047e-04, 1.29934648e-04, 1.48486783e-04, 1.66213635e-04, 1.79030249e-04, 1.85601544e-04, 1.88380363e-04, 1.38660631e-04, 1.41002165e-04, 5.10490616e-04, 5.97267004e-04, 5.10636659e-04, 4.25692560e-04, 3.60526814e-04, 3.14358331e-04, 2.77291780e-04, 2.28823061e-04, 1.92833613e-04, 1.59598058e-04, 1.22315978e-04, 1.07536085e-04, 9.86665691e-05, 8.83758039e-05, 4.32099259e-05, 3.16419428e-05, 3.62538267e-05, 3.29506183e-05, nan], dtype=float32) - Rig_T(zeuc)float64nan nan nan ... 0.09194 0.03173 nan
- long_name :
- $Ri^g_T$
array([ nan, nan, nan, nan, nan, 0.01489311, 0.03165001, 0.06313003, 0.14558223, 0.30405107, 0.5510997 , 0.84501221, 1.00159469, 1.10171097, 1.17564499, 1.25644404, 1.32506321, 1.30966974, 1.20444936, 1.09169364, 1.01902732, 0.98161728, 0.93856368, 0.95847886, 1.0277866 , 1.14108322, 1.30324548, 1.53638172, 1.83930923, 3.08740971, 5.92696916, 0.64201333, 0.39953276, 0.31802397, 0.27029139, 0.2428626 , 0.22293594, 0.2077806 , 0.19648165, 0.18172446, 0.16687778, 0.15812352, 0.15209324, 0.14781051, 0.16795355, 0.18188397, 0.11292903, 0.09193901, 0.03173482, nan]) - Tflx_dia_diff(zeuc)float64nan nan nan nan ... nan nan nan
- standard_name :
- ocean_vertical_diffusive_heat_flux
array([ nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, -5.17514235e-07, nan, -2.82599747e-06, -3.66443945e-07, -6.14955296e-06, -3.76138545e-06, -2.92809353e-06, -6.45150609e-06, -9.99721035e-06, -1.10884138e-05, -1.18222800e-05, -1.43206993e-05, -1.53016769e-05, -1.85974551e-05, -1.64466029e-05, -1.42208123e-05, -1.48474476e-05, -1.59149521e-05, -1.12224783e-05, -7.01141757e-06, -7.11490467e-06, -4.62302402e-06, -3.49574049e-06, -4.14221714e-06, nan, nan, nan]) - KT(zeuc)float64nan nan nan nan ... nan nan nan
- standard_name :
- ocean_vertical_heat_diffusivity
array([ nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 2.91855685e-05, nan, 2.63724818e-04, 5.17551881e-05, 5.16996775e-04, 2.38649440e-03, 4.78397845e-04, 4.52111916e-04, 6.96026866e-04, 9.52619969e-04, 1.08718966e-03, 1.55516006e-03, 2.27080410e-02, 2.70914629e-02, 6.55873581e-03, 4.65641779e-03, 2.04377327e-02, 2.38048398e-02, 1.97063766e-02, 2.76059209e-03, 2.76672216e-03, 2.04669252e-03, 2.47503727e-03, 2.86957564e-03, nan, nan, nan]) - ν(zeuc)float64nan nan nan nan ... nan nan nan nan
- standard_name :
- ocean_vertical_momentum_diffusivity
array([ nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 1.68499682e-05, 3.85379335e-05, 8.95743212e-04, 6.61870626e-04, 1.25469635e-03, 1.04469512e-03, 4.82821120e-04, 6.60574882e-04, 1.36210951e-03, 9.19967017e-04, 1.06437092e-03, 1.72836434e-03, 2.17503465e-03, 2.81499143e-03, 4.14499709e-03, 1.75873300e-02, 6.07257448e-03, 6.30236566e-03, 1.63667027e-02, 2.24646176e-03, 4.97174757e-06, nan, nan, nan, nan]) - chi(zeuc)float64nan nan nan nan ... nan nan nan
array([ nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 1.86298805e-08, nan, 6.29706882e-08, 6.09176283e-08, 9.08369013e-07, 4.42442169e-07, 4.39236144e-07, 1.11900126e-06, 1.65520285e-06, 1.57006590e-06, 1.24562823e-06, 1.13238867e-06, 9.20620793e-07, 8.59863821e-07, 6.37894873e-07, 4.62336468e-07, 4.36265962e-07, 4.36628547e-07, 2.59989298e-07, 1.10137167e-07, 1.40344814e-07, 5.56811423e-08, 1.42741529e-08, 1.62351688e-08, nan, nan, nan]) - eps(zeuc)float64nan nan nan nan ... nan nan nan
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<xarray.DatasetView> Dimensions: (zeuc: 50) Coordinates: latitude float32 0.0 longitude float32 -140.0 reference_pressure int64 0 * zeuc (zeuc) float64 -297.5 -287.5 -277.5 ... 182.5 192.5 Data variables: u (zeuc) float32 nan nan nan ... -0.2604 -0.3176 nan v (zeuc) float32 nan nan nan ... -0.02813 0.02461 nan theta (zeuc) float64 nan 11.76 11.79 ... 27.27 27.16 27.48 S2 (zeuc) float32 nan nan nan ... 3.625e-05 3.295e-05 nan Rig_T (zeuc) float64 nan nan nan nan ... 0.09194 0.03173 nan Tflx_dia_diff (zeuc) float64 nan nan nan nan ... nan nan nan KT (zeuc) float64 nan nan nan nan ... 0.00287 nan nan nan ν (zeuc) float64 nan nan nan nan nan ... nan nan nan nan chi (zeuc) float64 nan nan nan nan ... 1.624e-08 nan nan nan eps (zeuc) float64 nan nan nan nan ... 6.217e-08 nan nan nanTAO- zeuc: 50
- xh()float64-140.0
- cartesian_axis :
- X
- domain_decomposition :
- [220 222 220 221]
- long_name :
- h point nominal longitude
- units :
- degrees_east
array(-140.)
- yh()float640.0625
- cartesian_axis :
- Y
- domain_decomposition :
- [210 258 210 221]
- long_name :
- h point nominal latitude
- units :
- degrees_north
array(0.06249997)
- yq()float64-0.0625
- cartesian_axis :
- Y
- domain_decomposition :
- [209 257 209 221]
- long_name :
- q point nominal latitude
- units :
- degrees_north
array(-0.06249997)
- zeuc(zeuc)float64-297.5 -287.5 ... 182.5 192.5
array([-297.5, -287.5, -277.5, -267.5, -257.5, -247.5, -237.5, -227.5, -217.5, -207.5, -197.5, -187.5, -177.5, -167.5, -157.5, -147.5, -137.5, -127.5, -117.5, -107.5, -97.5, -87.5, -77.5, -67.5, -57.5, -47.5, -37.5, -27.5, -17.5, -7.5, 2.5, 12.5, 22.5, 32.5, 42.5, 52.5, 62.5, 72.5, 82.5, 92.5, 102.5, 112.5, 122.5, 132.5, 142.5, 152.5, 162.5, 172.5, 182.5, 192.5])
- uo(zeuc)float320.005257 -0.0184 0.0179 ... nan nan
- cell_methods :
- zl:mean yh:mean xq:point time: mean
- interp_method :
- none
- long_name :
- Sea Water X Velocity
- standard_name :
- sea_water_x_velocity
- time_avg_info :
- average_T1,average_T2,average_DT
- units :
- m s-1
array([ 0.00525669, -0.01840188, 0.01790354, 0.0044428 , -0.0186389 , 0.01406885, 0.0060294 , -0.01403717, 0.00815873, 0.00842694, 0.01887635, 0.0139282 , 0.02721404, 0.03995026, 0.0621833 , 0.08161839, 0.11901507, 0.14735334, 0.20004503, 0.23976822, 0.32848406, 0.3751118 , 0.4712556 , 0.52341986, 0.5774437 , 0.7075572 , 0.77591175, 0.8743804 , 0.9184269 , 1.0187924 , 0.9954761 , 0.9605037 , 0.7945212 , 0.57813746, 0.44689173, 0.33381617, 0.23095536, 0.12913482, 0.03992718, -0.02481052, -0.08560526, -0.15248425, -0.10183207, -0.08354276, -0.13608389, nan, nan, nan, nan, nan], dtype=float32) - vo(zeuc)float32-0.00127 1.53e-05 ... nan nan
- cell_methods :
- zl:mean yq:point xh:mean time: mean
- interp_method :
- none
- long_name :
- Sea Water Y Velocity
- standard_name :
- sea_water_y_velocity
- time_avg_info :
- average_T1,average_T2,average_DT
- units :
- m s-1
array([-1.2702544e-03, 1.5298558e-05, 1.3026360e-03, -1.1253332e-03, -8.5902464e-04, 1.3367438e-03, -1.2649365e-03, -1.2388808e-03, 6.0991739e-04, -3.4798661e-05, 3.7417454e-03, -6.4203995e-03, -1.3410304e-04, 7.5575250e-04, -1.5261871e-03, -8.9544582e-04, -2.8911787e-03, -2.2536672e-03, -3.4135813e-03, -3.5160836e-03, -4.5007500e-03, -5.0586360e-03, -4.4486639e-03, -5.7875998e-03, -5.1613864e-03, -6.5229321e-03, -6.9063683e-03, -7.4243234e-03, -5.4123211e-03, -3.4078853e-03, -7.4119419e-03, -8.3914027e-03, -2.5121963e-03, -3.6355543e-03, -1.0095604e-02, -1.7872766e-02, -2.3629947e-02, -2.7661886e-02, -2.9444154e-02, -2.7525136e-02, -2.4786126e-02, -1.9260338e-02, -1.8448964e-02, -1.3063808e-01, -1.2816702e-01, nan, nan, nan, nan, nan], dtype=float32) - ν(zeuc)float320.0002048 0.0002048 ... nan nan
- cell_methods :
- zl:mean yh:mean xq:point time: mean
- interp_method :
- none
- long_name :
- Total vertical viscosity at u-points
- standard_name :
- ocean_vertical_momentum_diffusivity
- time_avg_info :
- average_T1,average_T2,average_DT
- units :
- m2 s-1
array([0.00020476, 0.00020475, 0.00020475, 0.00020478, 0.00020481, 0.0002048 , 0.00020482, 0.00020483, 0.00020482, 0.00020483, 0.00020483, 0.0002049 , 0.00020487, 0.00020489, 0.00020487, 0.00020487, 0.0002049 , 0.00020492, 0.00020495, 0.00020499, 0.0002054 , 0.00020508, 0.00020601, 0.00020516, 0.00020499, 0.00020502, 0.00020504, 0.00020509, 0.00020512, 0.00020516, 0.00020516, 0.00020649, 0.00034668, 0.00085228, 0.00146995, 0.00216532, 0.0035074 , 0.00650569, 0.01049786, 0.01307183, 0.01283239, 0.01135312, 0.0113987 , 0.01385058, 0.00340031, nan, nan, nan, nan, nan], dtype=float32) - thetao(zeuc)float3210.1 10.37 10.47 ... nan nan nan
- cell_measures :
- volume: volcello area: areacello
- cell_methods :
- area:mean zl:mean yh:mean xh:mean time: mean
- long_name :
- Sea Water Potential Temperature
- standard_name :
- sea_water_potential_temperature
- time_avg_info :
- average_T1,average_T2,average_DT
- units :
- degC
array([10.102968 , 10.372348 , 10.466407 , 10.773228 , 11.006616 , 11.139993 , 11.4113245, 11.583537 , 11.776898 , 11.990218 , 12.162569 , 12.333334 , 12.339282 , 12.497538 , 12.593491 , 12.730412 , 12.862454 , 13.050186 , 13.259371 , 13.581723 , 14.052092 , 14.471857 , 15.148092 , 15.4848175, 16.043379 , 16.859596 , 17.563826 , 18.593836 , 19.781422 , 20.625542 , 21.567474 , 22.509794 , 23.978546 , 25.125193 , 25.493336 , 25.699633 , 25.858055 , 25.986782 , 26.08607 , 26.141712 , 26.089527 , 25.978794 , 26.172031 , 25.693325 , 25.745844 , nan, nan, nan, nan, nan], dtype=float32) - Tflx_dia_diff(zeuc)float321.993e-08 2.055e-08 ... nan nan
- cell_measures :
- area: areacello
- cell_methods :
- area:mean zi:point yh:mean xh:mean time: mean
- long_name :
- Diffusive diapycnal temperature flux across interfaces
- time_avg_info :
- average_T1,average_T2,average_DT
- units :
- degC m s-1
- standard_name :
- ocean_vertical_diffusive_heat_flux
array([1.99337329e-08, 2.05541344e-08, 2.09820854e-08, 2.12172608e-08, 2.17732055e-08, 2.11404103e-08, 1.88170048e-08, 2.02018402e-08, 1.85675297e-08, 1.67737362e-08, 1.47612083e-08, 1.39845033e-08, 1.43860861e-08, 1.45533212e-08, 1.68728480e-08, 2.18248424e-08, 2.00118091e-08, 2.62560054e-08, 3.27239533e-08, 4.31971259e-08, 5.48310979e-08, 7.83728638e-08, 8.56392361e-08, 7.29909928e-08, 7.89940344e-08, 7.16221535e-08, 8.24730719e-08, 8.97363108e-08, 1.14854465e-07, 1.16268268e-07, 8.87113742e-08, 6.96811924e-07, 6.34010848e-06, 1.42570707e-05, 1.82244657e-05, 1.80941097e-05, 1.87573423e-05, 2.04298794e-05, 2.28651788e-05, 2.33484861e-05, 2.10828657e-05, 1.31928864e-05, 1.17865256e-05, 2.07541343e-05, 4.46153081e-06, nan, nan, nan, nan, nan], dtype=float32) - Kd_heat(zeuc)float321.001e-06 1.001e-06 ... nan nan
- cell_measures :
- area: areacello
- cell_methods :
- area:mean zi:point yh:mean xh:mean time: mean
- long_name :
- Total diapycnal diffusivity for heat at interfaces
- time_avg_info :
- average_T1,average_T2,average_DT
- units :
- m2 s-1
- standard_name :
- ocean_vertical_heat_diffusivity
array([1.0006705e-06, 1.0006337e-06, 1.0006618e-06, 1.0006443e-06, 1.0006701e-06, 1.0006614e-06, 1.0006444e-06, 1.0006701e-06, 1.0006628e-06, 1.0106939e-06, 1.0401673e-06, 1.0006507e-06, 1.1119579e-06, 1.2560798e-06, 1.4861683e-06, 1.6268899e-06, 1.3251024e-06, 1.4133223e-06, 1.2183413e-06, 1.3440165e-06, 1.3007792e-06, 2.1559247e-06, 1.7224424e-06, 1.4074643e-06, 1.1677428e-06, 1.0357805e-06, 1.0051077e-06, 1.0014580e-06, 1.0006426e-06, 1.0018749e-06, 1.1331898e-06, 4.9953865e-06, 1.3441498e-04, 5.2427029e-04, 1.1975758e-03, 2.1821284e-03, 4.7503118e-03, 8.6501129e-03, 1.4562671e-02, 1.8755380e-02, 1.9549852e-02, 1.4596995e-02, 1.4510026e-02, 2.2565287e-02, 6.9412617e-03, nan, nan, nan, nan, nan], dtype=float32) - chi(zeuc)float327.983e-10 8.5e-10 ... nan nan
- long_name :
- $χ$
- units :
- C^2/s
array([7.98321798e-10, 8.50012727e-10, 8.84189610e-10, 9.11799580e-10, 9.56660640e-10, 9.04769315e-10, 7.18309079e-10, 8.29037838e-10, 7.05128622e-10, 5.75203885e-10, 4.42727882e-10, 4.47146736e-10, 4.10015882e-10, 3.96392363e-10, 4.70237904e-10, 7.21766591e-10, 7.88129451e-10, 1.20723875e-09, 2.13015827e-09, 3.33278893e-09, 5.25600186e-09, 7.23560190e-09, 9.75383418e-09, 8.95890473e-09, 1.27101201e-08, 1.15444925e-08, 1.67442575e-08, 2.00238528e-08, 2.98242746e-08, 3.13590256e-08, 1.84597369e-08, 2.74945506e-07, 1.06124980e-06, 1.65242091e-06, 1.08279119e-06, 7.10793756e-07, 4.89256706e-07, 3.81183526e-07, 3.18817285e-07, 3.70346726e-07, 4.42385783e-07, 5.16218051e-07, 4.75548546e-07, 3.79147195e-07, 8.43883186e-07, nan, nan, nan, nan, nan], dtype=float32) - eps(zeuc)float329.259e-11 1.049e-10 ... nan nan
- long_name :
- $ε$
- units :
- W/kg
array([9.25853075e-11, 1.04935380e-10, 1.02905920e-10, 1.32156480e-10, 1.15078946e-10, 1.16655560e-10, 1.43847920e-10, 1.19792704e-10, 1.41715001e-10, 1.54142199e-10, 2.68964850e-10, 6.02213612e-10, 3.46677242e-10, 7.41039119e-10, 1.00023889e-09, 1.85596560e-09, 2.51813637e-09, 3.90712618e-09, 5.12678300e-09, 7.66280817e-09, 8.26953261e-09, 1.09633360e-08, 1.24252670e-08, 1.39698848e-08, 1.54164272e-08, 1.55849254e-08, 1.44626435e-08, 1.37376457e-08, 1.04168105e-08, 2.90004687e-09, 4.27402691e-09, 1.44846027e-07, 1.99676307e-07, 3.04392813e-07, 2.78771381e-07, 3.50479127e-07, 4.06664299e-07, 4.18821770e-07, 3.92315798e-07, 3.80271871e-07, 4.16405783e-07, 4.76534012e-07, 4.59916748e-07, 2.96663188e-07, 4.61132771e-07, nan, nan, nan, nan, nan], dtype=float32) - S2(zeuc)float322.532e-07 3.085e-07 ... nan nan
- long_name :
- $S^2$
- units :
- s$^{-2}$
array([2.53197157e-07, 3.08510607e-07, 2.92727435e-07, 4.32506539e-07, 3.44354930e-07, 3.59136124e-07, 5.08331141e-07, 3.83767656e-07, 5.07877189e-07, 5.82732639e-07, 1.15930766e-06, 2.78240032e-06, 1.53309384e-06, 3.44462319e-06, 4.66998154e-06, 8.77538332e-06, 1.20398645e-05, 1.87341047e-05, 2.45930187e-05, 3.67302637e-05, 3.95981842e-05, 5.22509799e-05, 5.93275399e-05, 6.70128939e-05, 7.40271207e-05, 7.50657564e-05, 6.94298578e-05, 6.57120399e-05, 4.91650717e-05, 1.24117068e-05, 1.86605121e-05, 3.53242329e-04, 3.78591998e-04, 3.90216097e-04, 1.84304707e-04, 1.55016198e-04, 1.40105636e-04, 1.20296543e-04, 9.45677166e-05, 8.90597657e-05, 9.51775655e-05, 1.15308751e-04, 1.09259403e-04, 7.42343545e-05, 1.64657977e-04, nan, nan, nan, nan, nan], dtype=float32) - Rig_T(zeuc)float32215.0 202.1 206.4 ... nan nan nan
- long_name :
- $Ri^g_T$
array([2.1503494e+02, 2.0214403e+02, 2.0636514e+02, 2.0012900e+02, 1.8109155e+02, 2.0417029e+02, 1.3411453e+02, 1.9008124e+02, 1.5265700e+02, 1.5746408e+02, 4.6662010e+01, 1.1054462e+01, 2.8667643e+01, 8.2051058e+00, 5.2001052e+00, 3.1951575e+00, 2.6567822e+00, 2.2889338e+00, 2.2457385e+00, 1.9907213e+00, 2.3420768e+00, 2.0011907e+00, 1.9492234e+00, 1.8765500e+00, 2.0379128e+00, 2.0180886e+00, 2.5594373e+00, 2.9622624e+00, 5.6772432e+00, 3.1978878e+01, 1.7669277e+01, 1.5686954e+00, 1.0714781e+00, 5.2440548e-01, 3.9277917e-01, 2.9803014e-01, 2.4751794e-01, 2.4547334e-01, 3.0760974e-01, 3.3345494e-01, 3.1290802e-01, 1.8050675e-01, 1.9237757e-01, 2.6489717e-01, 1.3896098e-02, nan, nan, nan, nan, nan], dtype=float32)
<xarray.DatasetView> Dimensions: (zeuc: 50) Coordinates: xh float64 -140.0 yh float64 0.0625 yq float64 -0.0625 * zeuc (zeuc) float64 -297.5 -287.5 -277.5 ... 172.5 182.5 192.5 Data variables: uo (zeuc) float32 0.005257 -0.0184 0.0179 ... nan nan nan vo (zeuc) float32 -0.00127 1.53e-05 0.001303 ... nan nan nan ν (zeuc) float32 0.0002048 0.0002048 0.0002047 ... nan nan nan thetao (zeuc) float32 10.1 10.37 10.47 10.77 ... nan nan nan nan Tflx_dia_diff (zeuc) float32 1.993e-08 2.055e-08 2.098e-08 ... nan nan nan Kd_heat (zeuc) float32 1.001e-06 1.001e-06 1.001e-06 ... nan nan nan chi (zeuc) float32 7.983e-10 8.5e-10 8.842e-10 ... nan nan nan eps (zeuc) float32 9.259e-11 1.049e-10 1.029e-10 ... nan nan nan S2 (zeuc) float32 2.532e-07 3.085e-07 2.927e-07 ... nan nan nan Rig_T (zeuc) float32 215.0 202.1 206.4 200.1 ... nan nan nan nanbaseline.001- zeuc: 50
- xh()float64-140.0
- cartesian_axis :
- X
- domain_decomposition :
- [220 222 220 221]
- long_name :
- h point nominal longitude
- units :
- degrees_east
array(-140.)
- yh()float640.0625
- cartesian_axis :
- Y
- domain_decomposition :
- [210 258 210 221]
- long_name :
- h point nominal latitude
- units :
- degrees_north
array(0.06249997)
- yq()float64-0.0625
- cartesian_axis :
- Y
- domain_decomposition :
- [209 257 209 221]
- long_name :
- q point nominal latitude
- units :
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array(-0.06249997)
- zeuc(zeuc)float64-297.5 -287.5 ... 182.5 192.5
array([-297.5, -287.5, -277.5, -267.5, -257.5, -247.5, -237.5, -227.5, -217.5, -207.5, -197.5, -187.5, -177.5, -167.5, -157.5, -147.5, -137.5, -127.5, -117.5, -107.5, -97.5, -87.5, -77.5, -67.5, -57.5, -47.5, -37.5, -27.5, -17.5, -7.5, 2.5, 12.5, 22.5, 32.5, 42.5, 52.5, 62.5, 72.5, 82.5, 92.5, 102.5, 112.5, 122.5, 132.5, 142.5, 152.5, 162.5, 172.5, 182.5, 192.5])
- uo(zeuc)float320.004048 0.01676 ... nan nan
- cell_methods :
- zl:mean yh:mean xq:point time: mean
- interp_method :
- none
- long_name :
- Sea Water X Velocity
- standard_name :
- sea_water_x_velocity
- time_avg_info :
- average_T1,average_T2,average_DT
- units :
- m s-1
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- cell_methods :
- zl:mean yq:point xh:mean time: mean
- interp_method :
- none
- long_name :
- Sea Water Y Velocity
- standard_name :
- sea_water_y_velocity
- time_avg_info :
- average_T1,average_T2,average_DT
- units :
- m s-1
array([ 0.00104795, 0.00261766, -0.0018014 , 0.00037841, 0.0025488 , -0.00187899, -0.00026791, 0.00100588, 0.00070265, 0.00101431, 0.00384123, -0.00017991, 0.00037468, 0.00011675, -0.0017148 , -0.00150565, -0.00363744, -0.00322482, -0.0050785 , -0.00597607, -0.0056937 , -0.00600529, -0.0086971 , -0.00675561, -0.00828242, -0.00761547, -0.00865436, -0.00872748, -0.00841743, -0.00749545, -0.00702322, -0.00871168, -0.01047203, -0.01597641, -0.02200845, -0.02644102, -0.02778417, -0.02557627, -0.02107927, -0.01428133, -0.00863254, 0.00180638, 0.00251294, nan, nan, nan, nan, nan, nan, nan], dtype=float32) - ν(zeuc)float320.0002048 0.0002048 ... nan nan
- cell_methods :
- zl:mean yh:mean xq:point time: mean
- interp_method :
- none
- long_name :
- Total vertical viscosity at u-points
- standard_name :
- ocean_vertical_momentum_diffusivity
- time_avg_info :
- average_T1,average_T2,average_DT
- units :
- m2 s-1
array([0.00020475, 0.00020475, 0.00020479, 0.00020481, 0.00020481, 0.00020482, 0.00020483, 0.00020483, 0.00020483, 0.00020485, 0.00020483, 0.00020489, 0.00020488, 0.00020487, 0.00020488, 0.00020491, 0.00020493, 0.00020495, 0.00020495, 0.00020496, 0.00020496, 0.00020498, 0.000205 , 0.00020501, 0.00020503, 0.00020506, 0.0002051 , 0.00020513, 0.00020516, 0.00020519, 0.00020521, 0.00022332, 0.00031059, 0.00066837, 0.00124214, 0.00244679, 0.00480632, 0.00820587, 0.00956165, 0.01016004, 0.00855367, 0.00905411, 0.00920703, nan, nan, nan, nan, nan, nan, nan], dtype=float32) - thetao(zeuc)float3210.4 10.54 10.86 ... nan nan nan
- cell_measures :
- volume: volcello area: areacello
- cell_methods :
- area:mean zl:mean yh:mean xh:mean time: mean
- long_name :
- Sea Water Potential Temperature
- standard_name :
- sea_water_potential_temperature
- time_avg_info :
- average_T1,average_T2,average_DT
- units :
- degC
array([10.40172 , 10.537823 , 10.859438 , 11.050811 , 11.223505 , 11.51517 , 11.68402 , 11.896485 , 12.074736 , 12.329481 , 12.410086 , 12.484327 , 12.630804 , 12.72751 , 12.877808 , 12.997511 , 13.182478 , 13.378202 , 13.6936865, 14.069012 , 14.489995 , 15.146014 , 15.748816 , 16.187355 , 17.096003 , 17.545921 , 18.504902 , 19.545824 , 20.847746 , 21.178389 , 22.544098 , 23.383211 , 24.294075 , 25.223984 , 25.63944 , 25.85893 , 26.019669 , 26.134165 , 26.18872 , 26.17477 , 26.322565 , 26.62483 , 26.912878 , nan, nan, nan, nan, nan, nan, nan], dtype=float32) - Tflx_dia_diff(zeuc)float322.117e-08 2.133e-08 ... nan nan
- cell_measures :
- area: areacello
- cell_methods :
- area:mean zi:point yh:mean xh:mean time: mean
- long_name :
- Diffusive diapycnal temperature flux across interfaces
- time_avg_info :
- average_T1,average_T2,average_DT
- units :
- degC m s-1
- standard_name :
- ocean_vertical_diffusive_heat_flux
array([2.11659774e-08, 2.13343228e-08, 2.27206094e-08, 2.12139071e-08, 2.17982397e-08, 2.17070539e-08, 1.85763174e-08, 1.89413871e-08, 1.71070216e-08, 1.51437192e-08, 1.35404239e-08, 1.59176636e-08, 1.31053639e-08, 1.83535800e-08, 2.00722408e-08, 1.85805007e-08, 2.17158664e-08, 2.99371763e-08, 3.47126132e-08, 4.60725538e-08, 5.43710925e-08, 6.53851089e-08, 6.50946816e-08, 6.93698965e-08, 7.52715721e-08, 8.42500185e-08, 8.77352164e-08, 9.17952292e-08, 1.13108079e-07, 1.20545963e-07, 8.29441831e-08, 1.44966236e-07, 6.83658254e-06, 1.46440325e-05, 1.75006007e-05, 1.90046012e-05, 2.07540070e-05, 2.20161055e-05, 2.00347113e-05, 1.61481894e-05, 1.27846570e-05, 8.64653157e-06, 1.04316132e-05, nan, nan, nan, nan, nan, nan, nan], dtype=float32) - Kd_heat(zeuc)float321.001e-06 1.001e-06 ... nan nan
- cell_measures :
- area: areacello
- cell_methods :
- area:mean zi:point yh:mean xh:mean time: mean
- long_name :
- Total diapycnal diffusivity for heat at interfaces
- standard_name :
- ocean_vertical_heat_diffusivity
- time_avg_info :
- average_T1,average_T2,average_DT
- units :
- m2 s-1
array([1.0006451e-06, 1.0006506e-06, 1.0006515e-06, 1.0006414e-06, 1.0006618e-06, 1.0006513e-06, 1.0006434e-06, 1.0006655e-06, 1.0176084e-06, 1.0149389e-06, 1.0019072e-06, 1.2137010e-06, 1.0805032e-06, 1.4297108e-06, 1.4507829e-06, 1.1778233e-06, 1.1204596e-06, 1.1991365e-06, 1.0945305e-06, 1.0956771e-06, 1.1185289e-06, 1.2680930e-06, 1.1178845e-06, 1.0855989e-06, 1.0786908e-06, 1.0059720e-06, 1.0019743e-06, 1.0011390e-06, 1.0006572e-06, 1.0011006e-06, 1.1255088e-06, 1.8804314e-05, 1.1712696e-04, 4.8406073e-04, 1.1037614e-03, 3.1598986e-03, 6.9173714e-03, 1.1358041e-02, 1.3600050e-02, 1.3077446e-02, 1.2642518e-02, 1.0288579e-02, 1.1792418e-02, nan, nan, nan, nan, nan, nan, nan], dtype=float32) - chi(zeuc)float329.004e-10 9.152e-10 ... nan nan
- long_name :
- $χ$
- units :
- C^2/s
array([9.0043989e-10, 9.1522040e-10, 1.0396450e-09, 9.1001395e-10, 9.6060004e-10, 9.5469777e-10, 7.0223621e-10, 7.3680501e-10, 5.9830035e-10, 4.7491955e-10, 3.9812650e-10, 4.7236026e-10, 3.7199399e-10, 5.7536242e-10, 6.8126182e-10, 7.5827411e-10, 1.0768707e-09, 1.8212872e-09, 2.5872873e-09, 4.2573043e-09, 5.7363847e-09, 7.2878090e-09, 8.2911358e-09, 9.9289998e-09, 1.2009678e-08, 1.5639399e-08, 1.8246034e-08, 1.9992989e-08, 2.9070740e-08, 3.2245666e-08, 1.5850427e-08, 3.6572640e-08, 1.2725955e-06, 1.6085360e-06, 1.2024104e-06, 7.9952855e-07, 5.2454573e-07, 4.2225017e-07, 4.9829407e-07, 5.8574591e-07, 4.9689334e-07, 5.1183719e-07, 4.9258375e-07, nan, nan, nan, nan, nan, nan, nan], dtype=float32) - eps(zeuc)float321.218e-10 1.312e-10 ... nan nan
- long_name :
- $ε$
- units :
- W/kg
array([1.2180949e-10, 1.3122294e-10, 1.2108119e-10, 1.6528152e-10, 1.4035438e-10, 1.1820606e-10, 1.9289763e-10, 1.5263664e-10, 1.8540991e-10, 2.3599367e-10, 2.0692258e-10, 7.2218748e-10, 6.4210226e-10, 1.0354551e-09, 1.6377171e-09, 2.4149363e-09, 3.4051888e-09, 4.9914552e-09, 6.9494068e-09, 9.0220658e-09, 1.0754879e-08, 1.2555609e-08, 1.3969577e-08, 1.5608988e-08, 1.6270414e-08, 1.6123863e-08, 1.6080397e-08, 1.3522453e-08, 9.2339345e-09, 2.6684295e-09, 3.6976919e-09, 9.5134418e-08, 2.9680785e-07, 3.5708706e-07, 4.7353410e-07, 5.1698737e-07, 5.3716803e-07, 5.2572875e-07, 5.2556476e-07, 5.5077942e-07, 5.1904334e-07, 4.9139135e-07, 5.1879834e-07, nan, nan, nan, nan, nan, nan, nan], dtype=float32) - S2(zeuc)float323.838e-07 4.264e-07 ... nan nan
- long_name :
- $S^2$
- units :
- s$^{-2}$
array([3.8381995e-07, 4.2644211e-07, 3.6150070e-07, 5.9201142e-07, 4.6376638e-07, 3.5570309e-07, 7.5283435e-07, 5.5199490e-07, 7.2743495e-07, 9.9200213e-07, 8.6687038e-07, 3.3345341e-06, 2.9840382e-06, 4.8193274e-06, 7.7370651e-06, 1.1557462e-05, 1.6346772e-05, 2.3976709e-05, 3.3468012e-05, 4.3415948e-05, 5.1736562e-05, 6.0332572e-05, 6.7232111e-05, 7.5159194e-05, 7.8313511e-05, 7.7508535e-05, 7.7205652e-05, 6.4623826e-05, 4.3322329e-05, 1.1143856e-05, 1.5413378e-05, 2.9862832e-04, 6.0351175e-04, 4.6424806e-04, 3.6171466e-04, 2.8594036e-04, 2.1869803e-04, 1.7350054e-04, 1.7688426e-04, 1.9089431e-04, 1.7569039e-04, 1.9307368e-04, 1.8426294e-04, nan, nan, nan, nan, nan, nan, nan], dtype=float32) - Rig_T(zeuc)float32177.0 140.2 155.8 ... nan nan nan
- long_name :
- $Ri^g_T$
array([1.7700241e+02, 1.4017377e+02, 1.5582840e+02, 1.4044011e+02, 1.5148082e+02, 1.7303912e+02, 1.1180737e+02, 1.4587625e+02, 1.2738346e+02, 6.4905960e+01, 4.9831394e+01, 8.3011351e+00, 1.0125983e+01, 5.7563944e+00, 3.7067041e+00, 2.7272551e+00, 2.4012811e+00, 2.1475210e+00, 1.9123729e+00, 1.9800160e+00, 2.0326688e+00, 1.8785818e+00, 1.8993816e+00, 1.8516837e+00, 1.9699457e+00, 2.4552357e+00, 2.5303655e+00, 3.2691784e+00, 6.8624544e+00, 3.8340775e+01, 2.2407738e+01, 1.0736854e+00, 4.4377226e-01, 3.1085035e-01, 2.2195560e-01, 1.8700995e-01, 1.9031787e-01, 2.2211176e-01, 2.2589630e-01, 1.9110769e-01, 1.7184874e-01, 1.3849184e-01, 2.1321297e-01, nan, nan, nan, nan, nan, nan, nan], dtype=float32)
<xarray.DatasetView> Dimensions: (zeuc: 50) Coordinates: xh float64 -140.0 yh float64 0.0625 yq float64 -0.0625 * zeuc (zeuc) float64 -297.5 -287.5 -277.5 ... 172.5 182.5 192.5 Data variables: uo (zeuc) float32 0.004048 0.01676 0.006131 ... nan nan nan vo (zeuc) float32 0.001048 0.002618 -0.001801 ... nan nan nan ν (zeuc) float32 0.0002048 0.0002048 0.0002048 ... nan nan nan thetao (zeuc) float32 10.4 10.54 10.86 11.05 ... nan nan nan nan Tflx_dia_diff (zeuc) float32 2.117e-08 2.133e-08 2.272e-08 ... nan nan nan Kd_heat (zeuc) float32 1.001e-06 1.001e-06 1.001e-06 ... nan nan nan chi (zeuc) float32 9.004e-10 9.152e-10 1.04e-09 ... nan nan nan eps (zeuc) float32 1.218e-10 1.312e-10 1.211e-10 ... nan nan nan S2 (zeuc) float32 3.838e-07 4.264e-07 3.615e-07 ... nan nan nan Rig_T (zeuc) float32 177.0 140.2 155.8 140.4 ... nan nan nan nanbaseline.kpp.lmd.004- zeuc: 50
- xh()float64-140.0
- axis :
- X
- domain_decomposition :
- [220 222 220 221]
- long_name :
- h point nominal longitude
- units :
- degrees_east
array(-140.)
- yh()float640.0625
- axis :
- Y
- domain_decomposition :
- [210 258 210 221]
- long_name :
- h point nominal latitude
- units :
- degrees_north
array(0.06249997)
- yq()float64-0.0625
- axis :
- Y
- domain_decomposition :
- [209 257 209 221]
- long_name :
- q point nominal latitude
- units :
- degrees_north
array(-0.06249997)
- zeuc(zeuc)float64-297.5 -287.5 ... 182.5 192.5
array([-297.5, -287.5, -277.5, -267.5, -257.5, -247.5, -237.5, -227.5, -217.5, -207.5, -197.5, -187.5, -177.5, -167.5, -157.5, -147.5, -137.5, -127.5, -117.5, -107.5, -97.5, -87.5, -77.5, -67.5, -57.5, -47.5, -37.5, -27.5, -17.5, -7.5, 2.5, 12.5, 22.5, 32.5, 42.5, 52.5, 62.5, 72.5, 82.5, 92.5, 102.5, 112.5, 122.5, 132.5, 142.5, 152.5, 162.5, 172.5, 182.5, 192.5])
- uo(zeuc)float320.02499 -0.01359 ... nan nan
- cell_methods :
- zl:mean yh:mean xq:point time: mean
- interp_method :
- none
- long_name :
- Sea Water X Velocity
- standard_name :
- sea_water_x_velocity
- time_avg_info :
- average_T1,average_T2,average_DT
- units :
- m s-1
array([ 0.02499344, -0.01359064, 0.01056438, 0.02405857, -0.01237831, 0.01013298, 0.02191616, -0.00767961, 0.00422038, 0.00949787, 0.0014572 , 0.00401488, 0.01101161, 0.00240373, 0.01586246, 0.00856926, 0.03002909, 0.03372687, 0.07525551, 0.11013176, 0.18264885, 0.2575284 , 0.32371256, 0.41555193, 0.50246024, 0.6455064 , 0.75232065, 0.91769034, 1.0688589 , 1.1614565 , 1.1729106 , 1.0739865 , 0.78872025, 0.62034184, 0.49858826, 0.380375 , 0.27201495, 0.16802388, 0.07608254, 0.00831099, -0.04175798, -0.08453508, -0.04369244, 0.12923996, 0.08277461, nan, nan, nan, nan, nan], dtype=float32) - vo(zeuc)float320.001215 -0.0004066 ... nan nan
- cell_methods :
- zl:mean yq:point xh:mean time: mean
- interp_method :
- none
- long_name :
- Sea Water Y Velocity
- standard_name :
- sea_water_y_velocity
- time_avg_info :
- average_T1,average_T2,average_DT
- units :
- m s-1
array([ 0.00121472, -0.00040661, 0.00017783, 0.00129372, -0.00017322, 0.00046198, 0.00148334, -0.00020848, -0.00060944, 0.00136364, -0.00039485, -0.00453154, 0.00167347, -0.00063127, 0.00158471, -0.00220249, -0.00043014, -0.00388327, -0.0008026 , -0.00243983, 0.0007454 , 0.00054837, -0.00046543, -0.0001288 , -0.00214814, -0.00606339, -0.00876785, -0.00452488, -0.0027157 , -0.00483246, -0.01359488, -0.00209842, 0.00908873, 0.0029544 , -0.00833544, -0.01829557, -0.02495088, -0.02942124, -0.03080559, -0.02925389, -0.02618347, -0.02168874, -0.0189103 , -0.06369759, -0.05137211, nan, nan, nan, nan, nan], dtype=float32) - ν(zeuc)float325.046e-06 5.048e-06 ... nan nan
- cell_methods :
- zl:mean yh:mean xq:point time: mean
- interp_method :
- none
- long_name :
- Total vertical viscosity at u-points
- standard_name :
- ocean_vertical_momentum_diffusivity
- time_avg_info :
- average_T1,average_T2,average_DT
- units :
- m2 s-1
array([5.0462340e-06, 5.0475892e-06, 5.0424624e-06, 5.0440090e-06, 5.0467970e-06, 5.0418971e-06, 5.0431631e-06, 5.0451308e-06, 5.0414542e-06, 5.0420358e-06, 5.0389685e-06, 5.0458352e-06, 5.0413701e-06, 5.0399844e-06, 5.0401809e-06, 5.0381868e-06, 5.1015418e-06, 6.8741460e-06, 1.5321362e-05, 3.2997450e-05, 5.5269622e-05, 8.3268496e-05, 6.9354384e-05, 9.5276482e-05, 7.8466102e-05, 4.6640664e-05, 3.3242406e-05, 1.1284424e-05, 5.3480094e-06, 1.8180930e-05, 8.6516111e-06, 7.5917036e-05, 4.6185430e-04, 9.8257314e-04, 1.4048299e-03, 2.0107760e-03, 3.2659161e-03, 5.8874162e-03, 9.1227721e-03, 1.1989832e-02, 1.2402624e-02, 1.1848887e-02, 1.1853820e-02, 1.4631215e-02, 3.4738344e-03, nan, nan, nan, nan, nan], dtype=float32) - thetao(zeuc)float329.926 10.25 10.35 ... nan nan nan
- cell_measures :
- volume: volcello area: areacello
- cell_methods :
- area:mean zl:mean yh:mean xh:mean time: mean
- long_name :
- Sea Water Potential Temperature
- standard_name :
- sea_water_potential_temperature
- time_avg_info :
- average_T1,average_T2,average_DT
- units :
- degC
array([ 9.925618 , 10.254531 , 10.354003 , 10.53566 , 10.865824 , 10.970225 , 11.218256 , 11.542037 , 11.700399 , 12.0454 , 12.268132 , 12.709218 , 12.647938 , 12.898456 , 13.133925 , 13.318799 , 13.4945545, 13.652494 , 13.850175 , 14.109336 , 14.381747 , 14.7182045, 15.119492 , 15.392733 , 15.855298 , 16.483904 , 17.017002 , 18.144295 , 19.60642 , 20.326212 , 20.99324 , 22.676971 , 24.507181 , 25.129498 , 25.416185 , 25.618725 , 25.778563 , 25.90913 , 26.007828 , 26.056711 , 26.065672 , 26.052399 , 26.221981 , 26.439838 , 26.489485 , nan, nan, nan, nan, nan], dtype=float32) - Tflx_dia_diff(zeuc)float321.755e-08 1.924e-08 ... nan nan
- cell_measures :
- area: areacello
- cell_methods :
- area:mean zi:point yh:mean xh:mean time: mean
- long_name :
- Diffusive diapycnal temperature flux across interfaces
- standard_name :
- ocean_vertical_diffusive_heat_flux
- time_avg_info :
- average_T1,average_T2,average_DT
- units :
- degC m s-1
array([1.7554951e-08, 1.9238920e-08, 1.9244153e-08, 2.2659853e-08, 2.0953284e-08, 2.2788708e-08, 2.6120119e-08, 2.4164978e-08, 2.5351335e-08, 2.5231577e-08, 2.4755963e-08, 2.2278645e-08, 2.3270013e-08, 2.1283897e-08, 1.9231832e-08, 1.8315641e-08, 1.7733992e-08, 2.5324256e-08, 8.0707700e-08, 3.8407686e-07, 7.6808874e-07, 1.3642584e-06, 2.3333414e-06, 2.6994053e-06, 2.2198003e-06, 1.8574679e-06, 7.7228543e-07, 5.3184516e-07, 2.6284050e-07, 1.1193101e-07, 2.0913073e-07, 1.4701823e-05, 1.4408063e-05, 2.3515480e-05, 2.2960936e-05, 2.2215781e-05, 2.2256960e-05, 2.3240704e-05, 2.4761674e-05, 2.5259005e-05, 2.2617920e-05, 1.5300162e-05, 1.2066351e-05, 2.2057413e-05, 2.8274803e-06, nan, nan, nan, nan, nan], dtype=float32) - Kd_heat(zeuc)float321.001e-06 1.001e-06 ... nan nan
- cell_measures :
- area: areacello
- cell_methods :
- area:mean zi:point yh:mean xh:mean time: mean
- long_name :
- Total diapycnal diffusivity for heat at interfaces
- standard_name :
- ocean_vertical_heat_diffusivity
- time_avg_info :
- average_T1,average_T2,average_DT
- units :
- m2 s-1
array([1.00069019e-06, 1.00065347e-06, 1.00067587e-06, 1.00063551e-06, 1.00067132e-06, 1.00066575e-06, 1.00063346e-06, 1.00066700e-06, 1.00066563e-06, 1.00066734e-06, 1.00066416e-06, 1.00062528e-06, 1.00066745e-06, 1.00067314e-06, 1.00482680e-06, 1.07401365e-06, 1.08523659e-06, 1.39880501e-06, 4.77538333e-06, 2.90892385e-05, 4.31792432e-05, 6.99554439e-05, 9.46688160e-05, 9.24607375e-05, 7.00155651e-05, 5.26795411e-05, 1.31007955e-05, 5.59393993e-06, 2.53372355e-06, 1.77613265e-06, 1.33658182e-06, 2.10412065e-04, 3.71373346e-04, 8.84195731e-04, 1.33982731e-03, 2.16023228e-03, 4.38748859e-03, 7.71976588e-03, 1.28700137e-02, 1.76899210e-02, 1.95113998e-02, 1.61320884e-02, 1.55900959e-02, 2.49941684e-02, 7.19807623e-03, nan, nan, nan, nan, nan], dtype=float32) - chi(zeuc)float326.202e-10 7.44e-10 ... nan nan
- long_name :
- $χ$
- units :
- C^2/s
array([6.2018657e-10, 7.4400919e-10, 7.4519668e-10, 1.0408558e-09, 8.8766766e-10, 1.0477264e-09, 1.3862269e-09, 1.1847184e-09, 1.3015795e-09, 1.2918184e-09, 1.2502051e-09, 1.0106910e-09, 1.1095831e-09, 9.3900832e-10, 7.7561230e-10, 6.9214173e-10, 6.8843903e-10, 1.1982592e-09, 3.6828227e-09, 1.5802890e-08, 3.6510770e-08, 7.2088909e-08, 1.4925237e-07, 2.0199380e-07, 1.9517613e-07, 1.8122286e-07, 1.1386960e-07, 1.1739417e-07, 6.3101886e-08, 2.3677337e-08, 7.7539383e-08, 2.8464769e-06, 1.7457008e-06, 1.9661063e-06, 1.1580179e-06, 8.1946200e-07, 5.9891721e-07, 4.6636660e-07, 3.9778465e-07, 3.8270457e-07, 4.3542067e-07, 4.8403251e-07, 4.3925988e-07, 4.1374929e-07, 6.2473862e-07, nan, nan, nan, nan, nan], dtype=float32) - eps(zeuc)float323.79e-11 4.269e-11 ... nan nan
- long_name :
- $ε$
- units :
- W/kg
array([3.79035345e-11, 4.26914233e-11, 4.29057762e-11, 5.24651260e-11, 4.79460464e-11, 5.31061480e-11, 6.14722440e-11, 5.75307059e-11, 6.05433897e-11, 6.09162165e-11, 5.84663429e-11, 5.39510173e-11, 5.40397310e-11, 5.01869067e-11, 4.56859550e-11, 5.23621077e-11, 1.11069591e-10, 5.57708157e-10, 1.55664848e-09, 3.27052985e-09, 5.70149794e-09, 8.47223358e-09, 1.33512481e-08, 1.50298956e-08, 1.40175995e-08, 1.27147386e-08, 7.99094746e-09, 6.56495258e-09, 3.95846644e-09, 6.83437806e-10, 3.68607829e-08, 3.28708552e-07, 2.60447365e-07, 2.61275943e-07, 3.00201549e-07, 3.76866780e-07, 4.21417553e-07, 4.29890179e-07, 4.11924589e-07, 3.76672062e-07, 3.74530913e-07, 4.16727289e-07, 4.40827421e-07, 3.00241652e-07, 5.02966031e-07, nan, nan, nan, nan, nan], dtype=float32) - S2(zeuc)float322.995e-07 2.884e-07 ... nan nan
- long_name :
- $S^2$
- units :
- s$^{-2}$
array([2.99454200e-07, 2.88390282e-07, 3.94741505e-07, 5.30968293e-07, 4.65758774e-07, 5.12829388e-07, 5.89175841e-07, 6.17585215e-07, 6.72956560e-07, 8.13043584e-07, 7.72349836e-07, 6.12035194e-07, 6.96600068e-07, 7.94913888e-07, 7.87037322e-07, 1.80515713e-06, 4.14777469e-06, 1.13599381e-05, 2.49655386e-05, 3.86873107e-05, 5.25923242e-05, 6.51414230e-05, 8.23767332e-05, 1.08375476e-04, 1.27818377e-04, 1.52830122e-04, 1.93246451e-04, 2.14157830e-04, 2.23237090e-04, 5.20113535e-05, 1.84496806e-04, 8.95032368e-04, 6.05278881e-04, 2.61586159e-04, 1.78649512e-04, 1.69708088e-04, 1.52827561e-04, 1.26139494e-04, 1.06460036e-04, 9.21873580e-05, 9.53566050e-05, 1.12116184e-04, 1.05927014e-04, 7.23837584e-05, 1.64326877e-04, nan, nan, nan, nan, nan], dtype=float32) - Rig_T(zeuc)float32180.5 238.2 184.8 ... nan nan nan
- long_name :
- $Ri^g_T$
array([1.80487656e+02, 2.38210449e+02, 1.84815262e+02, 1.29452072e+02, 1.42724121e+02, 1.33232925e+02, 1.08492073e+02, 1.15649536e+02, 1.18179962e+02, 9.78238068e+01, 1.05259544e+02, 1.15983322e+02, 1.20559654e+02, 1.13338959e+02, 1.05069458e+02, 6.24187737e+01, 2.53599625e+01, 1.01569824e+01, 3.78092313e+00, 1.77504945e+00, 1.37038183e+00, 1.04456234e+00, 9.38269973e-01, 8.45975637e-01, 7.97548473e-01, 7.48805523e-01, 9.33551669e-01, 1.23177063e+00, 1.45686054e+00, 6.68316460e+00, 8.43145561e+00, 5.06967545e-01, 5.58340728e-01, 4.84673023e-01, 3.70436102e-01, 3.00139725e-01, 2.60080546e-01, 2.52664149e-01, 3.00420284e-01, 3.45842630e-01, 3.32556754e-01, 2.14251995e-01, 1.95444688e-01, 3.57639253e-01, 3.24461162e-02, nan, nan, nan, nan, nan], dtype=float32)
<xarray.DatasetView> Dimensions: (zeuc: 50) Coordinates: xh float64 -140.0 yh float64 0.0625 yq float64 -0.0625 * zeuc (zeuc) float64 -297.5 -287.5 -277.5 ... 172.5 182.5 192.5 Data variables: uo (zeuc) float32 0.02499 -0.01359 0.01056 ... nan nan nan vo (zeuc) float32 0.001215 -0.0004066 0.0001778 ... nan nan nan ν (zeuc) float32 5.046e-06 5.048e-06 5.042e-06 ... nan nan nan thetao (zeuc) float32 9.926 10.25 10.35 10.54 ... nan nan nan nan Tflx_dia_diff (zeuc) float32 1.755e-08 1.924e-08 1.924e-08 ... nan nan nan Kd_heat (zeuc) float32 1.001e-06 1.001e-06 1.001e-06 ... nan nan nan chi (zeuc) float32 6.202e-10 7.44e-10 7.452e-10 ... nan nan nan eps (zeuc) float32 3.79e-11 4.269e-11 4.291e-11 ... nan nan nan S2 (zeuc) float32 2.995e-07 2.884e-07 3.947e-07 ... nan nan nan Rig_T (zeuc) float32 180.5 238.2 184.8 129.5 ... nan nan nan nannew_baseline.hb- zeuc: 50
- xh()float64-140.0
- axis :
- X
- domain_decomposition :
- [220 222 220 221]
- long_name :
- h point nominal longitude
- units :
- degrees_east
array(-140.)
- yh()float640.0625
- axis :
- Y
- domain_decomposition :
- [210 258 210 221]
- long_name :
- h point nominal latitude
- units :
- degrees_north
array(0.06249997)
- yq()float64-0.0625
- axis :
- Y
- domain_decomposition :
- [209 257 209 221]
- long_name :
- q point nominal latitude
- units :
- degrees_north
array(-0.06249997)
- zeuc(zeuc)float64-297.5 -287.5 ... 182.5 192.5
array([-297.5, -287.5, -277.5, -267.5, -257.5, -247.5, -237.5, -227.5, -217.5, -207.5, -197.5, -187.5, -177.5, -167.5, -157.5, -147.5, -137.5, -127.5, -117.5, -107.5, -97.5, -87.5, -77.5, -67.5, -57.5, -47.5, -37.5, -27.5, -17.5, -7.5, 2.5, 12.5, 22.5, 32.5, 42.5, 52.5, 62.5, 72.5, 82.5, 92.5, 102.5, 112.5, 122.5, 132.5, 142.5, 152.5, 162.5, 172.5, 182.5, 192.5])
- uo(zeuc)float32-0.01065 0.02048 ... nan nan
- cell_methods :
- zl:mean yh:mean xq:point time: mean
- interp_method :
- none
- long_name :
- Sea Water X Velocity
- standard_name :
- sea_water_x_velocity
- time_avg_info :
- average_T1,average_T2,average_DT
- units :
- m s-1
array([-1.0646891e-02, 2.0477358e-02, 1.1050474e-02, -8.4186951e-03, 1.7257325e-02, 1.1625963e-02, -6.5456191e-03, 1.3428366e-02, -4.1635828e-03, 8.3168782e-03, -3.1610057e-04, -4.4213003e-03, 4.7586383e-03, -1.6103460e-03, 5.9380159e-03, 1.0472262e-02, 2.0668298e-02, 4.8041590e-02, 7.7255875e-02, 1.3745204e-01, 1.9449836e-01, 2.8766719e-01, 4.3414849e-01, 5.1080972e-01, 6.4297825e-01, 7.6984107e-01, 9.1899163e-01, 1.0664825e+00, 1.1405255e+00, 1.2641385e+00, 1.2902447e+00, 1.2021922e+00, 9.1631228e-01, 6.8355072e-01, 5.1118356e-01, 3.5848483e-01, 2.2015458e-01, 1.0378160e-01, 1.9374512e-02, -3.7018765e-02, -9.4496332e-02, -4.8449118e-02, -4.7313720e-02, 1.0204794e-01, 5.5657301e-02, nan, nan, nan, nan, nan], dtype=float32) - vo(zeuc)float32-0.0004901 0.0005714 ... nan nan
- cell_methods :
- zl:mean yq:point xh:mean time: mean
- interp_method :
- none
- long_name :
- Sea Water Y Velocity
- standard_name :
- sea_water_y_velocity
- time_avg_info :
- average_T1,average_T2,average_DT
- units :
- m s-1
array([-0.00049009, 0.00057143, -0.00114546, -0.00014106, 0.00080116, -0.00064699, -0.00018529, 0.00069077, 0.00067059, -0.0008774 , 0.00098835, -0.00173778, -0.00020317, -0.00044024, 0.00021668, -0.0002896 , -0.00128701, -0.00099582, -0.00125251, -0.00213526, -0.00148937, -0.0002665 , -0.00644217, -0.0011105 , -0.00474673, -0.00347817, -0.0042568 , -0.0022129 , -0.00164185, -0.00106187, -0.00665514, -0.0052943 , -0.00831086, -0.01532346, -0.02331558, -0.02841561, -0.02930233, -0.0267921 , -0.02194007, -0.01483669, -0.00246736, -0.00684613, -0.0120682 , 0.09584202, 0.11942156, nan, nan, nan, nan, nan], dtype=float32) - ν(zeuc)float325.046e-06 5.043e-06 ... nan nan
- cell_methods :
- zl:mean yh:mean xq:point time: mean
- interp_method :
- none
- long_name :
- Total vertical viscosity at u-points
- standard_name :
- ocean_vertical_momentum_diffusivity
- time_avg_info :
- average_T1,average_T2,average_DT
- units :
- m2 s-1
array([5.0464682e-06, 5.0427479e-06, 5.0452982e-06, 5.0455860e-06, 5.0420449e-06, 5.0442563e-06, 5.0445096e-06, 5.0414201e-06, 5.0418244e-06, 5.0409303e-06, 5.0384342e-06, 5.0429262e-06, 5.0398326e-06, 5.0403924e-06, 5.0384015e-06, 5.0397739e-06, 5.0431672e-06, 5.1221195e-06, 1.2207116e-05, 1.7747578e-05, 1.4220629e-05, 1.1861085e-05, 1.7739267e-05, 1.5002135e-05, 1.2204411e-05, 1.0666293e-05, 6.1089358e-06, 5.0663079e-06, 5.0405142e-06, 2.5051277e-05, 2.0913076e-05, 5.0343264e-05, 3.2911345e-04, 7.6059770e-04, 1.3257149e-03, 2.7636024e-03, 5.2530593e-03, 7.9990737e-03, 9.9148713e-03, 1.0457821e-02, 8.8949250e-03, 9.7109433e-03, 9.9065546e-03, 1.7521054e-02, 3.3595981e-03, nan, nan, nan, nan, nan], dtype=float32) - thetao(zeuc)float3210.29 10.42 10.62 ... nan nan nan
- cell_measures :
- volume: volcello area: areacello
- cell_methods :
- area:mean zl:mean yh:mean xh:mean time: mean
- long_name :
- Sea Water Potential Temperature
- standard_name :
- sea_water_potential_temperature
- time_avg_info :
- average_T1,average_T2,average_DT
- units :
- degC
array([10.287295 , 10.417949 , 10.615116 , 10.869449 , 11.04266 , 11.262654 , 11.567776 , 11.807474 , 12.077805 , 12.501555 , 12.587448 , 12.865992 , 13.096592 , 13.335708 , 13.5657015, 13.746414 , 13.889223 , 14.036531 , 14.221035 , 14.368961 , 14.648155 , 15.087256 , 15.539373 , 15.975408 , 16.745865 , 17.152761 , 18.009958 , 19.28983 , 20.677986 , 21.19363 , 22.335844 , 23.474924 , 24.749414 , 25.383026 , 25.68066 , 25.866386 , 26.00263 , 26.103117 , 26.162252 , 26.248213 , 26.393143 , 26.920116 , 27.855074 , 26.945793 , 26.995775 , nan, nan, nan, nan, nan], dtype=float32) - Tflx_dia_diff(zeuc)float321.85e-08 1.913e-08 ... nan nan
- cell_measures :
- area: areacello
- cell_methods :
- area:mean zi:point yh:mean xh:mean time: mean
- long_name :
- Diffusive diapycnal temperature flux across interfaces
- standard_name :
- ocean_vertical_diffusive_heat_flux
- time_avg_info :
- average_T1,average_T2,average_DT
- units :
- degC m s-1
array([1.85005504e-08, 1.91293861e-08, 2.06160440e-08, 2.21359180e-08, 2.24070380e-08, 2.48522607e-08, 2.56552166e-08, 2.63386593e-08, 2.74371743e-08, 2.67701328e-08, 2.69359877e-08, 2.44756340e-08, 2.41357050e-08, 2.14560352e-08, 1.97716332e-08, 1.66414509e-08, 1.74895423e-08, 2.14079350e-08, 3.45732083e-08, 6.12063857e-08, 7.47684510e-08, 1.62360351e-07, 2.77199149e-07, 4.46485075e-07, 3.56709933e-07, 1.23152688e-07, 1.46308878e-07, 1.06570774e-07, 1.38309090e-07, 1.07144494e-07, 1.11251758e-07, 5.28505052e-06, 1.73781937e-05, 1.98480011e-05, 2.11431343e-05, 2.20121365e-05, 2.35071038e-05, 2.38630219e-05, 2.23098959e-05, 1.71911925e-05, 1.30739172e-05, 8.85367081e-06, 9.64845640e-06, 2.54260249e-05, 3.50545884e-06, nan, nan, nan, nan, nan], dtype=float32) - Kd_heat(zeuc)float321.001e-06 1.001e-06 ... nan nan
- cell_measures :
- area: areacello
- cell_methods :
- area:mean zi:point yh:mean xh:mean time: mean
- long_name :
- Total diapycnal diffusivity for heat at interfaces
- standard_name :
- ocean_vertical_heat_diffusivity
- time_avg_info :
- average_T1,average_T2,average_DT
- units :
- m2 s-1
array([1.0006723e-06, 1.0006611e-06, 1.0006628e-06, 1.0006404e-06, 1.0006654e-06, 1.0006577e-06, 1.0006396e-06, 1.0006671e-06, 1.0006659e-06, 1.0006629e-06, 1.0006570e-06, 1.0006389e-06, 1.0006675e-06, 1.0019558e-06, 1.0135477e-06, 1.0423036e-06, 1.2229641e-06, 1.4524945e-06, 1.7310816e-05, 1.5045007e-05, 3.8584258e-06, 6.2752624e-06, 8.8590523e-06, 1.3132055e-05, 1.0210648e-05, 2.2401643e-06, 3.0851486e-06, 1.0444164e-06, 1.0006403e-06, 6.2561652e-05, 1.0642791e-06, 7.5733333e-05, 4.5665741e-04, 7.8447431e-04, 1.5393369e-03, 3.8610261e-03, 8.3111003e-03, 1.1603486e-02, 1.4628670e-02, 1.4060708e-02, 1.3308118e-02, 1.1162918e-02, 1.2060479e-02, 2.7877459e-02, 5.9886063e-03, nan, nan, nan, nan, nan], dtype=float32) - chi(zeuc)float326.88e-10 7.366e-10 ... nan nan
- long_name :
- $χ$
- units :
- C^2/s
array([6.8802869e-10, 7.3664508e-10, 8.5824386e-10, 9.8956543e-10, 1.0133299e-09, 1.2501800e-09, 1.3284884e-09, 1.4034628e-09, 1.5236361e-09, 1.4596947e-09, 1.4793824e-09, 1.2308681e-09, 1.2104571e-09, 9.6535835e-10, 8.2865687e-10, 5.9357264e-10, 5.8739025e-10, 8.6316454e-10, 1.1760135e-09, 2.8925231e-09, 4.5060946e-09, 1.1985256e-08, 2.2661380e-08, 4.0463782e-08, 3.4963129e-08, 1.8348556e-08, 2.2507315e-08, 2.5304370e-08, 4.1292687e-08, 2.5772588e-08, 2.9690797e-08, 1.0108992e-06, 2.0222787e-06, 1.5631721e-06, 1.1606248e-06, 8.0264130e-07, 5.3737216e-07, 4.3478525e-07, 4.4919923e-07, 5.4489811e-07, 4.6658363e-07, 4.9533986e-07, 3.4559400e-07, 3.4655343e-07, 4.7845720e-07, nan, nan, nan, nan, nan], dtype=float32) - eps(zeuc)float324.024e-11 4.357e-11 ... nan nan
- long_name :
- $ε$
- units :
- W/kg
array([4.0239829e-11, 4.3565509e-11, 4.6415576e-11, 5.2155967e-11, 5.3069691e-11, 5.9277278e-11, 6.3031767e-11, 6.5039168e-11, 6.8138634e-11, 6.6525035e-11, 6.4967774e-11, 5.9018707e-11, 5.7870639e-11, 5.0631468e-11, 5.2535771e-11, 4.8821273e-11, 8.2792731e-11, 2.4017247e-10, 3.2558109e-10, 5.9656929e-10, 9.9171138e-10, 2.1262123e-09, 2.7699996e-09, 3.7947077e-09, 3.7461754e-09, 1.7915618e-09, 1.9632773e-09, 1.3069008e-09, 2.7872606e-09, 5.1370547e-10, 2.4214209e-08, 2.9136123e-07, 2.7370675e-07, 3.9694919e-07, 4.9394691e-07, 5.2860804e-07, 5.0609714e-07, 4.7452161e-07, 4.8870527e-07, 5.5493587e-07, 5.5155920e-07, 5.5100037e-07, 6.9890541e-07, 1.1058072e-06, 2.2871466e-06, nan, nan, nan, nan, nan], dtype=float32) - S2(zeuc)float323.245e-07 5.718e-07 ... nan nan
- long_name :
- $S^2$
- units :
- s$^{-2}$
array([3.2451570e-07, 5.7184326e-07, 4.3148790e-07, 6.0657896e-07, 6.7605208e-07, 5.9352175e-07, 8.2924151e-07, 8.5900632e-07, 8.6205756e-07, 1.0500863e-06, 8.3261909e-07, 7.6358799e-07, 7.8491541e-07, 7.2332011e-07, 1.5663835e-06, 2.1174208e-06, 7.1297836e-06, 1.3430081e-05, 2.4580020e-05, 4.0981133e-05, 6.2299107e-05, 9.6839991e-05, 1.3706491e-04, 1.7594828e-04, 1.9717270e-04, 1.9812472e-04, 1.8732122e-04, 1.7612877e-04, 1.7498141e-04, 4.2245716e-05, 1.6423324e-04, 1.1454133e-03, 5.2541826e-04, 4.1192962e-04, 3.3999418e-04, 2.7700741e-04, 2.1282340e-04, 1.6937664e-04, 1.5941910e-04, 1.7767286e-04, 1.6364509e-04, 1.8765387e-04, 1.9949353e-04, 1.1106770e-04, 1.6636330e-04, nan, nan, nan, nan, nan], dtype=float32) - Rig_T(zeuc)float32186.3 128.5 132.1 ... nan nan nan
- long_name :
- $Ri^g_T$
array([1.8634216e+02, 1.2853084e+02, 1.3210474e+02, 1.1979617e+02, 1.0202711e+02, 1.0279958e+02, 9.7481445e+01, 8.4427910e+01, 9.3046837e+01, 8.0721817e+01, 1.0160206e+02, 9.5520500e+01, 9.3262207e+01, 1.0652971e+02, 5.7025261e+01, 4.3027679e+01, 1.1232176e+01, 4.4198289e+00, 1.9743326e+00, 1.4598441e+00, 1.1865945e+00, 9.0280277e-01, 7.5970727e-01, 6.7375970e-01, 6.7109162e-01, 8.1848937e-01, 1.0103055e+00, 1.4284228e+00, 2.1402125e+00, 1.0676737e+01, 7.4439979e+00, 3.3229971e-01, 3.1535965e-01, 2.4582326e-01, 1.9892570e-01, 1.7789739e-01, 1.8457843e-01, 2.1493007e-01, 2.4419188e-01, 1.9702415e-01, 1.6759664e-01, 1.4394848e-01, 1.8335819e-01, 3.7373954e-01, 5.9188493e-02, nan, nan, nan, nan, nan], dtype=float32)
<xarray.DatasetView> Dimensions: (zeuc: 50) Coordinates: xh float64 -140.0 yh float64 0.0625 yq float64 -0.0625 * zeuc (zeuc) float64 -297.5 -287.5 -277.5 ... 172.5 182.5 192.5 Data variables: uo (zeuc) float32 -0.01065 0.02048 0.01105 ... nan nan nan vo (zeuc) float32 -0.0004901 0.0005714 -0.001145 ... nan nan nan ν (zeuc) float32 5.046e-06 5.043e-06 5.045e-06 ... nan nan nan thetao (zeuc) float32 10.29 10.42 10.62 10.87 ... nan nan nan nan Tflx_dia_diff (zeuc) float32 1.85e-08 1.913e-08 2.062e-08 ... nan nan nan Kd_heat (zeuc) float32 1.001e-06 1.001e-06 1.001e-06 ... nan nan nan chi (zeuc) float32 6.88e-10 7.366e-10 8.582e-10 ... nan nan nan eps (zeuc) float32 4.024e-11 4.357e-11 4.642e-11 ... nan nan nan S2 (zeuc) float32 3.245e-07 5.718e-07 4.315e-07 ... nan nan nan Rig_T (zeuc) float32 186.3 128.5 132.1 119.8 ... nan nan nan nannew_baseline.kpp.lmd.004- zeuc: 50
- xh()float64-140.0
- axis :
- X
- domain_decomposition :
- [220 222 220 221]
- long_name :
- h point nominal longitude
- units :
- degrees_east
array(-140.)
- yh()float640.0625
- axis :
- Y
- domain_decomposition :
- [210 258 210 221]
- long_name :
- h point nominal latitude
- units :
- degrees_north
array(0.06249997)
- yq()float64-0.0625
- axis :
- Y
- domain_decomposition :
- [209 257 209 221]
- long_name :
- q point nominal latitude
- units :
- degrees_north
array(-0.06249997)
- zeuc(zeuc)float64-297.5 -287.5 ... 182.5 192.5
array([-297.5, -287.5, -277.5, -267.5, -257.5, -247.5, -237.5, -227.5, -217.5, -207.5, -197.5, -187.5, -177.5, -167.5, -157.5, -147.5, -137.5, -127.5, -117.5, -107.5, -97.5, -87.5, -77.5, -67.5, -57.5, -47.5, -37.5, -27.5, -17.5, -7.5, 2.5, 12.5, 22.5, 32.5, 42.5, 52.5, 62.5, 72.5, 82.5, 92.5, 102.5, 112.5, 122.5, 132.5, 142.5, 152.5, 162.5, 172.5, 182.5, 192.5])
- uo(zeuc)float32-0.005893 0.005001 ... nan nan
- cell_methods :
- zl:mean yh:mean xq:point time: mean
- interp_method :
- none
- long_name :
- Sea Water X Velocity
- standard_name :
- sea_water_x_velocity
- time_avg_info :
- average_T1,average_T2,average_DT
- units :
- m s-1
array([-5.8929874e-03, 5.0010150e-03, 2.4871632e-02, -1.3677386e-05, 4.5415116e-03, 2.2741454e-02, 6.3635116e-03, 2.4531148e-03, 1.5222704e-02, -1.3702812e-03, 1.2736225e-02, 1.6036917e-02, -4.4084731e-03, 1.6050644e-02, -1.1332115e-03, 2.5234869e-02, 1.7870275e-02, 5.3290766e-02, 7.1112156e-02, 1.2818980e-01, 1.8416606e-01, 2.5570861e-01, 4.1997769e-01, 4.7003269e-01, 5.9571236e-01, 7.3374313e-01, 8.8366783e-01, 1.0486763e+00, 1.1454952e+00, 1.2573735e+00, 1.2901310e+00, 1.1977949e+00, 8.7613827e-01, 6.3310784e-01, 4.5654660e-01, 3.0633986e-01, 1.8709742e-01, 9.5162839e-02, 2.5411908e-02, -2.0137414e-02, -6.1557423e-02, -4.7269031e-02, 8.9613520e-02, 2.3487601e-01, 1.9478835e-01, nan, nan, nan, nan, nan], dtype=float32) - vo(zeuc)float32-0.002198 0.0004198 ... nan nan
- cell_methods :
- zl:mean yq:point xh:mean time: mean
- interp_method :
- none
- long_name :
- Sea Water Y Velocity
- standard_name :
- sea_water_y_velocity
- time_avg_info :
- average_T1,average_T2,average_DT
- units :
- m s-1
array([-2.19777389e-03, 4.19814343e-04, 7.79381837e-04, -1.63761433e-03, 5.18743531e-04, 1.12472579e-03, -1.14625844e-03, 9.12419346e-04, 4.86612611e-04, 1.00931247e-04, 1.75473702e-04, -1.71095878e-03, 7.74093845e-04, -5.65544877e-04, 1.16517756e-03, -3.99415003e-04, -1.95945366e-04, -9.65662650e-04, -1.62107041e-04, -1.39739586e-03, 1.30683271e-04, -2.51332135e-03, -5.16847987e-03, -3.49007547e-03, -4.20303456e-03, -5.73705230e-03, -7.36995926e-03, -6.09641057e-03, 1.46760372e-04, -2.56395293e-03, -8.92745517e-03, -6.60287915e-03, -5.58094215e-03, -1.28454212e-02, -2.20838133e-02, -2.92490181e-02, -3.26998085e-02, -3.21489237e-02, -2.89497189e-02, -2.41536312e-02, -1.76583268e-02, -1.93542968e-02, -3.83493304e-02, -1.23079315e-01, -1.05593860e-01, nan, nan, nan, nan, nan], dtype=float32) - ν(zeuc)float325.049e-06 5.043e-06 ... nan nan
- cell_methods :
- zl:mean yh:mean xq:point time: mean
- interp_method :
- none
- long_name :
- Total vertical viscosity at u-points
- standard_name :
- ocean_vertical_momentum_diffusivity
- time_avg_info :
- average_T1,average_T2,average_DT
- units :
- m2 s-1
array([5.0486437e-06, 5.0429589e-06, 5.0442504e-06, 5.0475896e-06, 5.0422968e-06, 5.0433819e-06, 5.0462104e-06, 5.0418989e-06, 5.0425606e-06, 5.0407480e-06, 5.0382882e-06, 5.0448925e-06, 5.0402373e-06, 5.0409162e-06, 5.0385738e-06, 5.0399153e-06, 7.4432128e-06, 5.0412550e-06, 7.5570620e-06, 1.4555165e-05, 1.3303173e-05, 9.6282793e-06, 1.8854163e-05, 1.3644422e-05, 1.4976369e-05, 1.1149726e-05, 6.3058492e-06, 5.0542981e-06, 5.0390413e-06, 6.6572366e-06, 9.9026156e-06, 4.6662979e-05, 3.2871615e-04, 8.2944619e-04, 1.6036916e-03, 3.3080974e-03, 6.4689615e-03, 1.0309312e-02, 1.3183054e-02, 1.3386927e-02, 1.0917425e-02, 1.0970640e-02, 1.1653668e-02, 1.6986607e-02, 3.7777168e-03, nan, nan, nan, nan, nan], dtype=float32) - thetao(zeuc)float3210.23 10.36 10.51 ... nan nan nan
- cell_measures :
- volume: volcello area: areacello
- cell_methods :
- area:mean zl:mean yh:mean xh:mean time: mean
- long_name :
- Sea Water Potential Temperature
- standard_name :
- sea_water_potential_temperature
- time_avg_info :
- average_T1,average_T2,average_DT
- units :
- degC
array([10.228951 , 10.357553 , 10.5078 , 10.813727 , 10.959333 , 11.150814 , 11.475641 , 11.667459 , 11.963285 , 12.336132 , 12.506242 , 12.775228 , 12.946938 , 13.233289 , 13.456113 , 13.663487 , 13.828789 , 13.956572 , 14.125122 , 14.294209 , 14.496647 , 14.8735285, 15.366182 , 15.701226 , 16.427809 , 16.800098 , 17.615082 , 18.836851 , 20.428156 , 20.803474 , 21.94135 , 23.154457 , 24.625856 , 25.270407 , 25.569725 , 25.756596 , 25.884304 , 25.971855 , 26.026701 , 26.067236 , 26.111523 , 26.287266 , 26.828798 , 27.264927 , 27.29486 , nan, nan, nan, nan, nan], dtype=float32) - Tflx_dia_diff(zeuc)float321.845e-08 1.901e-08 ... nan nan
- cell_measures :
- area: areacello
- cell_methods :
- area:mean zi:point yh:mean xh:mean time: mean
- long_name :
- Diffusive diapycnal temperature flux across interfaces
- standard_name :
- ocean_vertical_diffusive_heat_flux
- time_avg_info :
- average_T1,average_T2,average_DT
- units :
- degC m s-1
array([1.84522388e-08, 1.90133331e-08, 1.98160350e-08, 2.17871534e-08, 2.19269296e-08, 2.31987691e-08, 2.58633701e-08, 2.56593573e-08, 2.66053508e-08, 2.70146554e-08, 2.64776752e-08, 2.54793733e-08, 2.51310990e-08, 2.30840840e-08, 2.02013766e-08, 1.97913277e-08, 2.01755146e-08, 2.69346945e-08, 2.62779469e-08, 5.28789563e-08, 5.48875114e-08, 1.00109666e-07, 2.71216010e-07, 3.19583648e-07, 3.74500303e-07, 1.46047938e-07, 1.16343891e-07, 1.03832775e-07, 1.41212709e-07, 1.06493850e-07, 1.35873535e-07, 6.90023035e-06, 1.85962454e-05, 2.29003454e-05, 2.42327587e-05, 2.49604745e-05, 2.66399857e-05, 2.77722211e-05, 2.66196348e-05, 2.17321231e-05, 1.58120238e-05, 9.36440847e-06, 1.10887204e-05, 2.19333415e-05, 1.07785513e-06, nan, nan, nan, nan, nan], dtype=float32) - Kd_heat(zeuc)float321.001e-06 1.001e-06 ... nan nan
- cell_measures :
- area: areacello
- cell_methods :
- area:mean zi:point yh:mean xh:mean time: mean
- long_name :
- Total diapycnal diffusivity for heat at interfaces
- standard_name :
- ocean_vertical_heat_diffusivity
- time_avg_info :
- average_T1,average_T2,average_DT
- units :
- m2 s-1
array([1.00067518e-06, 1.00065427e-06, 1.00066927e-06, 1.00063642e-06, 1.00066666e-06, 1.00066268e-06, 1.00063505e-06, 1.00066575e-06, 1.00066745e-06, 1.00066438e-06, 1.00066154e-06, 1.00063460e-06, 1.00101636e-06, 1.00151851e-06, 1.00441002e-06, 1.06810353e-06, 1.41212877e-06, 1.91577578e-06, 1.80881364e-06, 1.60107575e-05, 2.37403265e-06, 4.09625409e-06, 9.22853906e-06, 2.46128784e-05, 1.08849845e-05, 4.00248064e-06, 2.07656421e-06, 1.03766467e-06, 1.00063471e-06, 1.10138728e-06, 1.06313692e-05, 8.53215824e-05, 4.45734942e-04, 9.08712565e-04, 1.99472369e-03, 4.84049134e-03, 1.07121104e-02, 1.64499823e-02, 2.01246608e-02, 1.98929477e-02, 1.70502812e-02, 1.29411258e-02, 1.47804301e-02, 3.19275931e-02, 6.53247582e-03, nan, nan, nan, nan, nan], dtype=float32) - chi(zeuc)float326.837e-10 7.26e-10 ... nan nan
- long_name :
- $χ$
- units :
- C^2/s
array([6.8373696e-10, 7.2600542e-10, 7.9185863e-10, 9.5713926e-10, 9.6860342e-10, 1.0881023e-09, 1.3508273e-09, 1.3291162e-09, 1.4311416e-09, 1.4834852e-09, 1.4281516e-09, 1.3238612e-09, 1.3013337e-09, 1.1062754e-09, 8.5938778e-10, 8.0222251e-10, 6.7409117e-10, 8.7976160e-10, 9.4508401e-10, 2.2733280e-09, 3.1751306e-09, 6.8161152e-09, 2.0803506e-08, 2.8093355e-08, 3.7279484e-08, 1.6553953e-08, 1.9269990e-08, 2.3974705e-08, 4.3170928e-08, 2.6435435e-08, 3.5509224e-08, 1.4844380e-06, 2.2649865e-06, 1.8430437e-06, 1.2526549e-06, 7.6951369e-07, 4.7593264e-07, 3.4695140e-07, 3.5711125e-07, 4.3343695e-07, 4.5873477e-07, 4.6846111e-07, 4.6900448e-07, 2.3183331e-07, 4.6221166e-07, nan, nan, nan, nan, nan], dtype=float32) - eps(zeuc)float323.938e-11 4.352e-11 ... nan nan
- long_name :
- $ε$
- units :
- W/kg
array([3.9382265e-11, 4.3520621e-11, 4.4468890e-11, 4.9679094e-11, 5.0937501e-11, 5.5393547e-11, 6.1908964e-11, 6.2446576e-11, 6.6434858e-11, 6.7113939e-11, 6.4494368e-11, 6.1906279e-11, 6.0327979e-11, 5.5024558e-11, 5.1699981e-11, 7.5435810e-11, 7.5297879e-11, 1.4965926e-10, 2.8711039e-10, 4.2676571e-10, 5.8342109e-10, 1.5079648e-09, 2.8221063e-09, 2.9051124e-09, 4.2336281e-09, 2.1570059e-09, 1.5742398e-09, 1.4037076e-09, 1.5026101e-09, 8.5145674e-10, 2.4366905e-08, 3.5581684e-07, 3.2794830e-07, 4.5730107e-07, 5.0692626e-07, 4.9011328e-07, 4.2123892e-07, 3.4453245e-07, 3.3985438e-07, 3.6136080e-07, 3.8029071e-07, 4.1178455e-07, 4.1902607e-07, 2.2534654e-07, 4.4791264e-07, nan, nan, nan, nan, nan], dtype=float32) - S2(zeuc)float322.544e-07 6.384e-07 ... nan nan
- long_name :
- $S^2$
- units :
- s$^{-2}$
array([2.54378733e-07, 6.38353811e-07, 4.58427763e-07, 3.80050096e-07, 5.64058951e-07, 6.36048640e-07, 5.56854729e-07, 7.26399946e-07, 9.05369006e-07, 9.45442423e-07, 8.51951029e-07, 6.91803280e-07, 7.55714382e-07, 7.04647334e-07, 1.26243197e-06, 2.71247109e-06, 4.65858102e-06, 1.16520087e-05, 1.99510814e-05, 3.31415249e-05, 5.00230417e-05, 8.05205273e-05, 1.27244726e-04, 1.69002291e-04, 2.03346484e-04, 2.10992672e-04, 1.87548270e-04, 1.89796614e-04, 2.03424614e-04, 5.08211524e-05, 1.83125841e-04, 1.33370166e-03, 6.43725973e-04, 4.54566209e-04, 3.34972632e-04, 2.33477040e-04, 1.48811640e-04, 1.01950354e-04, 9.15324563e-05, 9.83889113e-05, 1.07818625e-04, 1.17441712e-04, 1.18318312e-04, 4.21129625e-05, 1.39109572e-04, nan, nan, nan, nan, nan], dtype=float32) - Rig_T(zeuc)float32226.6 131.1 150.5 ... nan nan nan
- long_name :
- $Ri^g_T$
array([ 2.26648071e+02, 1.31057510e+02, 1.50471497e+02, 1.48722946e+02, 1.45639999e+02, 1.06133774e+02, 1.36979568e+02, 1.12499794e+02, 8.02135468e+01, 8.84140244e+01, 8.54823303e+01, 1.19611740e+02, 9.99403992e+01, 1.19359146e+02, 7.77576904e+01, 4.18875122e+01, 1.76356297e+01, 4.95762348e+00, 2.53250837e+00, 1.47906017e+00, 1.34763014e+00, 9.56021309e-01, 7.78292775e-01, 6.81925893e-01, 6.09881163e-01, 6.72925949e-01, 9.91986275e-01, 1.26318133e+00, 1.82970357e+00, 8.63165855e+00, 6.78957748e+00, 3.14613789e-01, 3.16555321e-01, 2.50890136e-01, 2.09905684e-01, 2.02570021e-01, 2.29070097e-01, 2.94496417e-01, 3.63696158e-01, 3.25371534e-01, 2.26018295e-01, 1.37651816e-01, 2.03731701e-01, 3.91990632e-01, -2.14977749e-03, nan, nan, nan, nan, nan], dtype=float32)
<xarray.DatasetView> Dimensions: (zeuc: 50) Coordinates: xh float64 -140.0 yh float64 0.0625 yq float64 -0.0625 * zeuc (zeuc) float64 -297.5 -287.5 -277.5 ... 172.5 182.5 192.5 Data variables: uo (zeuc) float32 -0.005893 0.005001 0.02487 ... nan nan nan vo (zeuc) float32 -0.002198 0.0004198 0.0007794 ... nan nan nan ν (zeuc) float32 5.049e-06 5.043e-06 5.044e-06 ... nan nan nan thetao (zeuc) float32 10.23 10.36 10.51 10.81 ... nan nan nan nan Tflx_dia_diff (zeuc) float32 1.845e-08 1.901e-08 1.982e-08 ... nan nan nan Kd_heat (zeuc) float32 1.001e-06 1.001e-06 1.001e-06 ... nan nan nan chi (zeuc) float32 6.837e-10 7.26e-10 7.919e-10 ... nan nan nan eps (zeuc) float32 3.938e-11 4.352e-11 4.447e-11 ... nan nan nan S2 (zeuc) float32 2.544e-07 6.384e-07 4.584e-07 ... nan nan nan Rig_T (zeuc) float32 226.6 131.1 150.5 148.7 ... nan nan nan nannew_baseline.kpp.lmd.005- zeuc: 80
- longitude()int64-140
- units :
- degrees_east
- standard_name :
- longitude
array(-140)
- latitude()int640
- units :
- degrees_north
- standard_name :
- latitude
array(0)
- zeuc(zeuc)float64-200.0 -195.0 ... 190.0 195.0
array([-200., -195., -190., -185., -180., -175., -170., -165., -160., -155., -150., -145., -140., -135., -130., -125., -120., -115., -110., -105., -100., -95., -90., -85., -80., -75., -70., -65., -60., -55., -50., -45., -40., -35., -30., -25., -20., -15., -10., -5., 0., 5., 10., 15., 20., 25., 30., 35., 40., 45., 50., 55., 60., 65., 70., 75., 80., 85., 90., 95., 100., 105., 110., 115., 120., 125., 130., 135., 140., 145., 150., 155., 160., 165., 170., 175., 180., 185., 190., 195.])
- u(zeuc)float64nan nan nan nan ... nan nan nan nan
- standard_name :
- sea_water_x_velocity
array([ nan, nan, nan, nan, nan, nan, nan, -1.05116857e-01, -8.78450123e-02, -6.91070307e-02, -5.93865995e-02, -3.65194950e-02, -3.06684922e-02, -3.16731008e-02, -1.45563634e-02, -1.04912230e-02, 1.20081768e-02, 3.44010920e-02, 6.06641421e-02, 8.70872427e-02, 1.14793442e-01, 1.43454882e-01, 1.73844347e-01, 2.06472059e-01, 2.41247369e-01, 2.80579111e-01, 3.23215957e-01, 3.69795305e-01, 4.17526619e-01, 4.64899298e-01, 5.10044100e-01, 5.53948504e-01, 5.97009535e-01, 6.39443865e-01, 6.81379689e-01, 7.22712552e-01, 7.63980042e-01, 8.04971029e-01, 8.45197885e-01, 8.73555811e-01, 8.88815998e-01, 8.71057848e-01, 8.38077529e-01, 7.90123658e-01, 7.42882018e-01, 6.95473878e-01, 6.46854003e-01, 5.93667994e-01, 5.36247881e-01, 4.71006770e-01, 4.01361037e-01, 3.26968218e-01, 2.52710497e-01, 1.80637561e-01, 1.19099493e-01, 8.03703500e-02, 2.75534902e-02, 7.20406459e-04, -3.41486551e-02, -4.16155778e-02, -2.64509151e-02, -4.66886898e-02, 5.56134438e-03, 1.38017142e-03, -1.42177546e-02, -7.06127976e-02, -8.76528601e-02, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan]) - v(zeuc)float64nan nan nan nan ... nan nan nan nan
- standard_name :
- sea_water_y_velocity
array([ nan, nan, nan, nan, nan, nan, nan, -0.0558892 , -0.04796128, -0.05296166, -0.05187718, -0.05491471, -0.05786042, -0.05031479, -0.05070498, -0.05010691, -0.05009191, -0.04864032, -0.04718164, -0.04498721, -0.04109011, -0.03560548, -0.02710676, -0.01679335, -0.0053954 , 0.00591623, 0.01682267, 0.02704529, 0.03553181, 0.04248355, 0.04656708, 0.04639887, 0.04212086, 0.02909312, 0.00998746, -0.01614186, -0.04189481, -0.06243535, -0.067715 , -0.06181233, -0.04935251, -0.0389986 , -0.03414252, -0.03764416, -0.05212121, -0.07260562, -0.10013674, -0.12669765, -0.15128597, -0.1691306 , -0.18148522, -0.18683141, -0.18550657, -0.18087429, -0.17383764, -0.17143653, -0.16175374, -0.16328311, -0.15892244, -0.14943801, -0.11895927, -0.1219241 , -0.14898413, -0.20467592, -0.20930846, -0.30027357, -0.30225361, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan]) - chi(zeuc)float64nan nan nan nan ... nan nan nan nan
- long_name :
- $χ$
- units :
- °C²/s
array([ nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 1.91401590e-09, 8.73531007e-08, 1.53549044e-08, 1.13987005e-08, 7.03480507e-08, 3.93812460e-08, 5.69824765e-08, 8.18564238e-08, 3.64788931e-08, 2.77012136e-08, 5.39047386e-08, 1.13209980e-07, 1.75043127e-07, 3.58997616e-07, 2.33415460e-07, 3.04582011e-07, 3.13033807e-07, 3.38606638e-07, 3.92352344e-07, 3.32935266e-07, 1.62167490e-07, 1.21872951e-07, 7.37120413e-08, 5.97562621e-08, 7.17358497e-08, 1.63791402e-07, 1.88997112e-07, 1.60624235e-07, 1.43209813e-07, 1.59526349e-07, 2.48843309e-07, 3.76894085e-07, 3.66777005e-07, 2.70499440e-07, 2.55378151e-07, 1.91707271e-07, 1.37017866e-07, 1.27049993e-07, 2.49129621e-07, 1.00105959e-07, 1.65623701e-07, 1.20560773e-07, 1.44200478e-07, 1.75966048e-07, 5.08732810e-07, 1.21455284e-06, 1.64655056e-06, 3.09695489e-08, 6.56333657e-08, 2.32187670e-09, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan]) - eps(zeuc)float64nan nan nan nan ... nan nan nan nan
- long_name :
- $ε$
- units :
- W/kg
array([ nan, nan, nan, nan, nan, nan, nan, nan, 1.04672174e-09, 2.46027970e-09, 1.12435161e-09, 1.28390414e-09, 1.00616878e-09, 1.03519860e-09, 2.96260747e-09, 2.08299195e-09, 5.10680596e-09, 7.47771585e-09, 1.57125503e-08, 7.88768013e-09, 8.44971424e-09, 4.95405167e-09, 6.47206807e-09, 7.24672927e-09, 7.54171659e-09, 6.65710039e-09, 5.62058178e-09, 4.73399454e-09, 7.06065555e-09, 3.53968618e-09, 4.21119364e-09, 4.99713646e-09, 9.33822508e-09, 5.78856419e-09, 7.65369289e-09, 9.16852766e-09, 1.66989484e-08, 1.66589623e-08, 1.43223281e-08, 1.27336669e-08, 1.65764687e-08, 1.45352688e-08, 1.73806699e-08, 1.44375924e-08, 3.41709034e-08, 8.32098814e-08, 1.44047673e-07, 2.14100181e-07, 2.97231235e-07, 3.63849720e-07, 4.39013202e-07, 4.86744389e-07, 5.54237391e-07, 5.81733022e-07, 6.24812775e-07, 6.33522725e-07, 6.83208750e-07, 1.03267754e-06, 7.95513562e-07, 9.53926772e-07, 1.49332115e-06, 1.57735953e-06, 3.85363994e-06, 3.48126797e-06, 7.35445242e-06, 5.35391261e-06, 5.19481001e-06, 1.93969621e-05, 5.62386531e-08, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan]) - theta(zeuc)float64nan nan nan nan ... nan nan nan nan
- standard_name :
- sea_water_potential_temperature
- units :
- celsius
array([ nan, nan, nan, nan, nan, nan, nan, nan, 12.27027015, 12.31477067, 12.60446926, 12.61411099, 12.5681978 , 12.5983967 , 12.64335497, 12.69678939, 12.85765037, 13.00071564, 13.20561109, 13.37259677, 13.47598497, 13.52191615, 13.77936605, 13.90548921, 14.07952953, 14.33257073, 14.63686104, 14.99768353, 15.38784395, 15.87271286, 16.48411809, 17.19243887, 18.04835956, 18.95035525, 19.90508981, 20.93727464, 21.89309668, 22.80955607, 23.67475671, 24.41619341, 25.04854397, 25.56969387, 25.96256023, 26.22942591, 26.42898395, 26.56141693, 26.64492918, 26.70014623, 26.73929252, 26.77195295, 26.81311731, 26.86566579, 26.91848789, 26.96277522, 27.00184556, 27.03331133, 27.05972579, 27.08007154, 27.0952071 , 27.1081003 , 27.11752904, 27.122601 , 27.1254335 , 27.12694655, 27.11912955, 27.13898699, 27.14391606, 27.1580728 , 27.17226852, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan]) - Sh2(zeuc)float64nan nan nan nan ... nan nan nan nan
array([ nan, nan, nan, nan, nan, nan, nan, nan, 2.41936817e-05, 2.63153069e-05, 3.56675431e-05, 2.53119561e-05, 2.23424096e-05, 1.98043466e-05, 1.84729444e-05, 2.19671534e-05, 2.55924596e-05, 3.08315806e-05, 3.57826475e-05, 3.92997466e-05, 4.34068094e-05, 4.95598280e-05, 5.85714661e-05, 6.75501495e-05, 7.88186807e-05, 9.27058518e-05, 1.04684731e-04, 1.12020167e-04, 1.12273605e-04, 1.07391366e-04, 1.04506594e-04, 1.07225977e-04, 1.12363100e-04, 1.23013305e-04, 1.33253630e-04, 1.40435138e-04, 1.43914091e-04, 1.29277376e-04, 9.69778004e-05, 5.85841049e-05, 3.12493265e-05, 5.75280338e-05, 1.07158583e-04, 1.51674537e-04, 1.67300940e-04, 1.77406931e-04, 1.90591406e-04, 2.04971901e-04, 2.23599647e-04, 2.38536376e-04, 2.43809555e-04, 2.41444550e-04, 2.30190300e-04, 2.11015658e-04, 1.83191649e-04, 1.48703654e-04, 1.19003331e-04, 8.52937072e-05, 7.24674164e-05, 6.83340417e-05, 4.64024226e-05, 6.59667504e-05, 9.51011300e-05, 3.11837692e-05, 6.31130409e-05, 2.33018435e-05, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan]) - Tz(zeuc)float64nan nan nan nan ... nan nan nan nan
array([ nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.01319911, 0.01055727, 0.00408227, 0.00488797, 0.00833917, 0.00741158, 0.00619215, 0.00669396, 0.01442379, 0.02392398, 0.03373917, 0.0364512 , 0.0419686 , 0.04242911, 0.04994164, 0.0578379 , 0.06513217, 0.07386418, 0.07939579, 0.08917988, 0.11031311, 0.13354579, 0.15769182, 0.17695173, 0.18580005, 0.19878145, 0.19871063, 0.18724572, 0.178166 , 0.16066373, 0.13737873, 0.11535005, 0.09140163, 0.06596186, 0.04663675, 0.03319472, 0.02159774, 0.01388406, 0.00943907, 0.00718388, 0.00742707, 0.00940052, 0.01054695, 0.00990347, 0.00847902, 0.00724818, 0.00586937, 0.00505891, 0.00477037, 0.00396721, 0.00339247, 0.00320092, 0.00324608, 0.00281531, 0.00241565, 0.002715 , 0.00087841, 0.00044906, 0.00022746, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan]) - N2T(zeuc)float64nan nan nan nan ... nan nan nan nan
- units :
- celsius
array([ nan, nan, nan, nan, nan, nan, nan, nan, nan, 2.42101685e-05, 1.96627179e-05, 7.61049420e-06, 9.09576015e-06, 1.55491325e-05, 1.38574145e-05, 1.16139629e-05, 1.26612997e-05, 2.74865039e-05, 4.60659926e-05, 6.55148621e-05, 7.11595780e-05, 8.21432717e-05, 8.40681817e-05, 9.95655518e-05, 1.16259825e-04, 1.32449602e-04, 1.52261760e-04, 1.66248694e-04, 1.89831521e-04, 2.39509079e-04, 2.97038622e-04, 3.60333355e-04, 4.17217932e-04, 4.52149709e-04, 4.99510667e-04, 5.16165310e-04, 5.00854737e-04, 4.89559732e-04, 4.52377409e-04, 3.94718017e-04, 3.37037767e-04, 2.70711323e-04, 1.97346088e-04, 1.40481314e-04, 1.00497690e-04, 6.56088486e-05, 4.22684175e-05, 2.87789886e-05, 2.19272064e-05, 2.26908933e-05, 2.87529707e-05, 3.23051213e-05, 3.03770943e-05, 2.60393452e-05, 2.22835101e-05, 1.80608336e-05, 1.55791284e-05, 1.46999338e-05, 1.22312804e-05, 1.04641459e-05, 9.87706905e-06, 1.00192077e-05, 8.69157880e-06, 7.45918718e-06, 8.38334162e-06, 2.71403662e-06, 1.38783523e-06, 7.03326675e-07, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan]) - Rig_T(zeuc)float64nan nan nan nan ... nan nan nan nan
- units :
- celsius
array([ nan, nan, nan, nan, nan, nan, nan, nan, nan, 1.99202459, 1.69606609, 0.56792074, 0.42689249, 0.51455311, 0.40304173, 0.29015853, 0.23716566, 0.51976126, 0.86862424, 1.55562163, 1.20180377, 1.15973112, 1.17351158, 1.23276115, 1.23035239, 1.15519236, 1.07573885, 1.03428242, 1.28023235, 1.91065333, 2.7633041 , 3.41448597, 3.76235454, 4.11676705, 4.10232985, 4.20455427, 3.92901596, 4.72556873, 5.88266212, 8.82068599, 14.15497927, 4.33564004, 1.21310963, 0.62287698, 0.44596426, 0.27578452, 0.15370404, 0.08466717, 0.06990047, 0.07796601, 0.09266341, 0.10296131, 0.10505684, 0.10214939, 0.09734437, 0.10989984, 0.12384845, 0.15676421, 0.16643647, 0.16322899, 0.26793743, 0.2305186 , 0.13040248, 0.26167545, 0.27165878, 0.30828336, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan]) - Shred2(zeuc)float64nan nan nan nan ... nan nan nan nan
array([ nan, nan, nan, nan, nan, nan, nan, nan, nan, -8.46871250e-05, -6.70577397e-05, -1.81609661e-05, -2.47504947e-05, -5.24407007e-05, -4.71447447e-05, -3.60356112e-05, -3.60513691e-05, -8.50070432e-05, -1.49780057e-04, -2.15055522e-04, -2.22929408e-04, -2.58418750e-04, -2.61600901e-04, -3.14968692e-04, -3.78199509e-04, -4.33549024e-04, -5.02047643e-04, -5.51514803e-04, -6.47338150e-04, -8.52258354e-04, -1.08612286e-03, -1.33591904e-03, -1.55803782e-03, -1.68500092e-03, -1.86344321e-03, -1.92265385e-03, -1.85813608e-03, -1.82851757e-03, -1.71277002e-03, -1.52039380e-03, -1.31661436e-03, -1.02461127e-03, -6.81412302e-04, -4.09453971e-04, -2.34192306e-04, -8.50317932e-05, 2.09196167e-05, 8.89778649e-05, 1.35051531e-04, 1.46642996e-04, 1.27572298e-04, 1.11304380e-04, 1.09318793e-04, 1.07693281e-04, 9.53061106e-05, 7.77479600e-05, 5.87095732e-05, 3.01430978e-05, 2.57882192e-05, 1.94371501e-05, 5.23228687e-06, 1.90434434e-05, 3.86272281e-05, -5.51034484e-06, -5.46780544e-06, -5.43243899e-06, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan]) - KT(zeuc)float64nan nan nan nan ... nan nan nan nan
- long_name :
- KT
- units :
- °C²/s
- standard_name :
- ocean_vertical_heat_diffusivity
array([ nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 3.11034257e-06, 7.27904762e-07, 7.10238685e-07, 3.85490340e-06, 7.62858065e-06, 4.23798353e-05, 4.26767225e-04, 1.92089081e-05, 3.21080997e-05, 1.68690567e-05, 2.60959572e-04, 5.25633464e-04, 1.08588017e-04, 3.56067721e-03, 1.54237668e-04, 1.38328616e-03, 2.17445864e-03, 3.35261789e-05, 9.29949055e-05, 1.15035765e-04, 1.27553983e-04, 3.47318661e-04, 4.74906977e-04, 5.43061763e-04, 1.28363424e-03, 2.14045291e-03, 3.90373232e-03, 1.82978622e-03, 2.49007649e-03, 5.53119349e-03, 4.12953073e-03, 3.21330613e-03, 4.31628907e-03, 1.98163881e-03, 3.30108160e-03, 3.77763230e-03, 1.05707982e-02, 8.16199487e-03, 7.87422784e-03, 1.03365032e-02, 2.47680837e-02, 1.68225290e-02, 1.66146718e-02, 3.34472636e-02, 4.69596923e-02, 6.39420715e-02, 4.61705915e-02, 5.40689455e-02, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan]) - ν(zeuc)float64nan nan nan nan ... nan nan nan nan
- long_name :
- ν
- units :
- W/kg
- standard_name :
- ocean_vertical_momentum_diffusivity
array([ nan, nan, nan, nan, nan, nan, nan, nan, nan, 2.02433025e-04, 6.52778817e-05, 6.56160982e-05, 8.95604155e-05, 7.05365382e-05, 3.98064010e-04, 1.52595744e-04, 1.91511453e-04, 6.08683341e-04, 7.73383850e-04, 3.66858201e-04, 3.20232402e-04, 1.13794554e-04, 1.18652299e-04, 1.40950282e-04, 1.54905537e-04, 9.73632499e-05, 6.87962805e-05, 5.31487061e-05, 1.09741835e-04, 4.98080835e-05, 6.27093545e-05, 7.50910423e-05, 1.70996185e-04, 9.45308237e-05, 9.90430163e-05, 1.20742021e-04, 2.10593434e-04, 2.11730363e-04, 2.07588177e-04, 3.26472478e-04, 6.62253420e-04, 3.79178084e-04, 2.28515027e-04, 1.16251384e-04, 2.70686576e-04, 3.60658923e-04, 7.08755437e-04, 1.27751012e-03, 1.43025791e-03, 1.76987787e-03, 2.19161082e-03, 2.35499497e-03, 2.87559556e-03, 3.38494818e-03, 3.65112011e-03, 4.99381967e-03, 6.09707376e-03, 8.01378558e-03, 1.10191331e-02, 9.18495381e-03, 1.21835999e-02, 1.51177039e-02, 9.63162607e-03, 3.72289668e-02, 5.69256543e-03, 2.53126173e-03, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan])
- starttime :
- ['' '' '' ... 'Time:22:51:04 328 ' 'Time:22:57:35 328 ' 'Time:23:04:04 328 ']
- endtime :
- ['' '' '' ... 'Time:22:54:39 328 ' 'Time:23:01:10 328 ' 'Time:23:07:49 328 ']
- readme :
- ['EPSILON_clean cleaned using tw91_eps_chi_sum1.mat ' '(all that is marked NaN or missed in tw91_eps_chi_sum1.mat ' 'is marked NaN in that field too) plus bad_drops.40, ' 'which contained contaminated casts, is used to mark bad EPSILON']
- name :
- TIWE
<xarray.DatasetView> Dimensions: (zeuc: 80) Coordinates: longitude int64 -140 latitude int64 0 * zeuc (zeuc) float64 -200.0 -195.0 -190.0 -185.0 ... 185.0 190.0 195.0 Data variables: u (zeuc) float64 nan nan nan nan nan nan ... nan nan nan nan nan v (zeuc) float64 nan nan nan nan nan nan ... nan nan nan nan nan chi (zeuc) float64 nan nan nan nan nan nan ... nan nan nan nan nan eps (zeuc) float64 nan nan nan nan nan nan ... nan nan nan nan nan theta (zeuc) float64 nan nan nan nan nan nan ... nan nan nan nan nan Sh2 (zeuc) float64 nan nan nan nan nan nan ... nan nan nan nan nan Tz (zeuc) float64 nan nan nan nan nan nan ... nan nan nan nan nan N2T (zeuc) float64 nan nan nan nan nan nan ... nan nan nan nan nan Rig_T (zeuc) float64 nan nan nan nan nan nan ... nan nan nan nan nan Shred2 (zeuc) float64 nan nan nan nan nan nan ... nan nan nan nan nan KT (zeuc) float64 nan nan nan nan nan nan ... nan nan nan nan nan ν (zeuc) float64 nan nan nan nan nan nan ... nan nan nan nan nan Attributes: starttime: ['' '' '' ... 'Time:22:51:04 328 ' 'Time:22:57:35 328 '\n... endtime: ['' '' '' ... 'Time:22:54:39 328 ' 'Time:23:01:10 328 '\n... readme: ['EPSILON_clean cleaned using tw91_eps_chi_sum1.mat ... name: TIWETIWE- zeuc: 80
- zeuc(zeuc)float64-200.0 -195.0 ... 190.0 195.0
array([-200., -195., -190., -185., -180., -175., -170., -165., -160., -155., -150., -145., -140., -135., -130., -125., -120., -115., -110., -105., -100., -95., -90., -85., -80., -75., -70., -65., -60., -55., -50., -45., -40., -35., -30., -25., -20., -15., -10., -5., 0., 5., 10., 15., 20., 25., 30., 35., 40., 45., 50., 55., 60., 65., 70., 75., 80., 85., 90., 95., 100., 105., 110., 115., 120., 125., 130., 135., 140., 145., 150., 155., 160., 165., 170., 175., 180., 185., 190., 195.])
- u(zeuc)float64nan nan nan nan ... nan nan nan nan
- standard_name :
- sea_water_x_velocity
array([ nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.22115788, 0.22654945, 0.29191623, 0.34394949, 0.3729316 , 0.38517439, 0.40121283, 0.42977609, 0.4662755 , 0.50906621, 0.55494024, 0.60390715, 0.65761372, 0.71461533, 0.77128995, 0.83032185, 0.89511044, 0.96070988, 1.02436224, 1.0902707 , 1.1546409 , 1.21010393, 1.26473946, 1.3234391 , 1.36673891, 1.30642576, 1.18996499, 1.05949794, 0.9322365 , 0.80925976, 0.69112074, 0.58224709, 0.48456937, 0.39901329, 0.32261591, 0.2510615 , 0.17860259, 0.10402496, 0.02954474, -0.03894944, -0.09192208, -0.13323597, -0.17259708, -0.21783335, -0.2211524 , -0.17832167, -0.05484485, 0.20941225, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan]) - v(zeuc)float64nan nan nan nan ... nan nan nan nan
- standard_name :
- sea_water_y_velocity
array([ nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, -0.10866021, -0.08189667, -0.0415053 , -0.01654078, -0.01723334, -0.02194068, -0.01866929, -0.01492488, -0.00601155, 0.00588569, 0.01844987, 0.03073579, 0.04198478, 0.05074239, 0.05639361, 0.05873327, 0.05652131, 0.05013584, 0.04227849, 0.03166786, 0.0239972 , 0.02739307, 0.03479125, 0.03245866, 0.01181622, -0.02180246, -0.05192954, -0.07314375, -0.08928198, -0.10649081, -0.12856883, -0.15245701, -0.17388981, -0.19018318, -0.20160852, -0.20642006, -0.20513149, -0.19960923, -0.19233737, -0.18394684, -0.1725463 , -0.16773698, -0.17698639, -0.22702554, -0.19803006, -0.23943411, -0.08621481, 0.66289347, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan]) - chi(zeuc)float64nan nan nan nan ... nan nan nan nan
- long_name :
- $χ$
- units :
- °C²/s
array([ nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 1.14494094e-10, 1.31958541e-10, 1.91693766e-08, 1.46419404e-08, 1.21253803e-08, 1.32856317e-08, 1.23397882e-08, 2.91354841e-08, 3.07858540e-08, 3.36049027e-08, 2.27920876e-08, 2.44964076e-08, 1.78190065e-08, 1.75669311e-08, 2.85808906e-08, 1.49093214e-07, 3.63759931e-07, 3.48106623e-07, 1.86058880e-07, 9.86207350e-08, 1.79307372e-07, 2.70481800e-07, 1.95429169e-07, 1.76120062e-07, 2.26235626e-07, 2.08672841e-06, 4.26421905e-06, 4.72083990e-06, 4.78804391e-06, 5.69365153e-06, 5.86931860e-06, 4.85199160e-06, 5.51234583e-06, 4.97715339e-06, 1.33977243e-05, 2.20134844e-06, 2.81166126e-06, 1.50208453e-06, 1.38140749e-06, 7.40429394e-06, 1.20634636e-06, 3.05004447e-06, 1.62756379e-04, 2.59531267e-06, 2.95864739e-06, 1.51474209e-06, 2.99036803e-06, 3.76726187e-06, 4.12241223e-07, 4.01809429e-07, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan]) - eps(zeuc)float64nan nan nan nan ... nan nan nan nan
- long_name :
- $ε$
- units :
- W/kg
array([ nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 5.32419426e-09, 2.45807692e-09, 4.10523158e-09, 4.09770973e-09, 4.35521884e-09, 3.99843721e-09, 6.32214805e-09, 5.35798845e-09, 6.72184206e-09, 6.93901553e-09, 6.31028114e-09, 5.87525040e-09, 5.71214151e-09, 5.18111550e-09, 5.13345064e-09, 7.72822196e-09, 1.92496201e-08, 1.86357852e-08, 1.39084977e-08, 9.70494225e-09, 1.33954526e-08, 1.69618118e-08, 2.04721725e-08, 1.83251281e-08, 2.66416423e-08, 2.92761399e-07, 7.80004637e-07, 1.18254903e-06, 1.35894506e-06, 1.53038001e-06, 1.54821424e-06, 1.49303914e-06, 1.41502900e-06, 1.47521170e-06, 1.45609523e-06, 1.52113361e-06, 1.57851807e-06, 1.62467500e-06, 1.61982032e-06, 1.65306750e-06, 1.81416930e-06, 2.38185475e-06, 3.09447452e-06, 4.57120561e-06, 5.21569277e-06, 7.15001194e-06, 9.22694251e-06, 8.08168171e-06, 6.98544200e-06, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan]) - theta(zeuc)float64nan nan nan nan ... nan nan nan nan
- standard_name :
- sea_water_potential_temperature
- units :
- celsius
array([ nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 12.87624238, 13.01483644, 13.15381329, 13.24011816, 13.29555921, 13.30639973, 13.34170064, 13.43829315, 13.58342779, 13.76042136, 13.93697868, 14.14579384, 14.38379946, 14.63648322, 14.92743999, 15.28545856, 15.74013707, 16.2102157 , 16.65955107, 17.16281705, 17.75368041, 18.38168465, 18.85633917, 19.35605905, 20.01964621, 20.75432354, 21.39860009, 21.88022006, 22.29196158, 22.66613606, 23.02317819, 23.3730267 , 23.7091678 , 24.0107391 , 24.25476328, 24.44696177, 24.59248381, 24.71203164, 24.81512293, 24.90538273, 24.98098258, 25.03964729, 25.07854227, 25.10922601, 25.12141223, 25.1390445 , 25.11738002, 25.04148045, 24.91115893, 24.87990414, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan]) - Sh2(zeuc)float64nan nan nan nan ... nan nan nan nan
array([ nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 1.46805278e-05, 2.06247109e-05, 2.79099890e-05, 4.23866561e-05, 5.11994357e-05, 6.33161535e-05, 7.02602956e-05, 8.38146306e-05, 9.79962718e-05, 1.10410829e-04, 1.25932413e-04, 1.45260588e-04, 1.56914383e-04, 1.63750445e-04, 1.92023306e-04, 2.22768575e-04, 2.20763479e-04, 2.20006645e-04, 2.36385131e-04, 2.09275090e-04, 1.73425476e-04, 1.80434080e-04, 1.81536156e-04, 1.79866699e-04, 6.27124566e-04, 9.32668894e-04, 9.12921742e-04, 8.10805421e-04, 7.32960667e-04, 6.63357940e-04, 5.79124548e-04, 4.88951792e-04, 3.97653283e-04, 3.19528934e-04, 2.80381527e-04, 2.73227861e-04, 2.78100963e-04, 2.71834086e-04, 2.45855867e-04, 2.10678972e-04, 1.72479992e-04, 1.53969878e-04, 1.11599259e-04, 8.92671008e-05, 4.89363817e-05, 1.42668450e-05, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan]) - Tz(zeuc)float64nan nan nan nan ... nan nan nan nan
array([ nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 3.44367380e-03, 6.22888616e-03, 1.04712061e-02, 1.57161617e-02, 1.82320296e-02, 2.33686425e-02, 3.01791295e-02, 3.75057690e-02, 4.07030220e-02, 4.31830180e-02, 4.73058646e-02, 5.01065707e-02, 5.45995780e-02, 6.49529271e-02, 8.12991949e-02, 9.24973936e-02, 9.18497953e-02, 9.51663275e-02, 1.09394235e-01, 1.21876151e-01, 1.10245938e-01, 9.74265472e-02, 1.16330704e-01, 1.39825363e-01, 1.37861577e-01, 1.12509470e-01, 8.92971678e-02, 7.85915995e-02, 7.31132631e-02, 7.06607877e-02, 6.86087732e-02, 6.38010404e-02, 5.45427007e-02, 4.36279223e-02, 3.37846019e-02, 2.64612821e-02, 2.21992763e-02, 1.93526017e-02, 1.66171053e-02, 1.36099913e-02, 1.11664335e-02, 9.35016297e-03, 8.20059551e-03, 6.34605231e-03, 5.02814275e-03, 3.56957173e-03, 1.98858752e-03, -4.23771129e-05, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan]) - N2T(zeuc)float64nan nan nan nan ... nan nan nan nan
- units :
- celsius
array([ nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 6.56721408e-06, 1.19643843e-05, 2.02057808e-05, 3.04211779e-05, 3.53250677e-05, 4.53737309e-05, 5.88906580e-05, 7.37172501e-05, 8.06924547e-05, 8.63397784e-05, 9.55211629e-05, 1.02298239e-04, 1.12757311e-04, 1.35879203e-04, 1.72720834e-04, 2.00283801e-04, 2.02711343e-04, 2.13789148e-04, 2.50548854e-04, 2.85333614e-04, 2.64005710e-04, 2.37222934e-04, 2.88109271e-04, 3.53930875e-04, 3.57196413e-04, 2.97354435e-04, 2.39448443e-04, 2.13319176e-04, 2.00622381e-04, 1.95890338e-04, 1.92094066e-04, 1.80316353e-04, 1.55437323e-04, 1.25164913e-04, 9.74349545e-05, 7.66192410e-05, 6.44902443e-05, 5.63807491e-05, 4.85326304e-05, 3.98341494e-05, 3.27371421e-05, 2.74444120e-05, 2.40933475e-05, 1.86538501e-05, 1.47891933e-05, 1.04952643e-05, 5.83671031e-06, -1.23994795e-07, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan]) - Rig_T(zeuc)float64nan nan nan nan ... nan nan nan nan
- units :
- celsius
array([ nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.69034727, 0.54732533, 0.67701297, 0.66380184, 0.59054073, 0.59983133, 0.71771542, 0.81118442, 0.80488672, 0.78706258, 0.77798954, 0.70695326, 0.70017751, 0.87434193, 0.95134817, 0.90612647, 0.91506717, 1.02694396, 1.13293163, 1.51519289, 1.6748384 , 1.46195153, 1.75690015, 2.69457106, 0.60679189, 0.34545704, 0.24627558, 0.23295552, 0.22900769, 0.23821688, 0.26834039, 0.29961545, 0.31962308, 0.31103159, 0.26839199, 0.23462162, 0.21331184, 0.20353174, 0.2067661 , 0.21799492, 0.23800667, 0.25086958, 0.3151814 , 0.39298945, 0.60493939, 0.41753704, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan]) - Shred2(zeuc)float64nan nan nan nan ... nan nan nan nan
array([ nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, -9.41249006e-06, -2.26975495e-05, -4.88815768e-05, -7.55817321e-05, -8.66440345e-05, -1.14995917e-04, -1.61245757e-04, -2.08176699e-04, -2.23806639e-04, -2.35229015e-04, -2.57068084e-04, -2.64991984e-04, -2.94913958e-04, -3.80309518e-04, -4.98993560e-04, -5.78022658e-04, -5.89577378e-04, -6.34761977e-04, -7.65620370e-04, -9.32094472e-04, -8.82516540e-04, -7.68429789e-04, -9.70903378e-04, -1.23664331e-03, -8.03395454e-04, -2.57748577e-04, -4.51092058e-05, -4.24450350e-05, -6.92698022e-05, -1.20411670e-04, -1.90716746e-04, -2.33448100e-04, -2.23928057e-04, -1.80979909e-04, -1.09652256e-04, -3.35175226e-05, 1.95853141e-05, 4.51908428e-05, 4.75623258e-05, 4.02442053e-05, 2.86345504e-05, 2.32317480e-05, -2.40244511e-06, -1.34170855e-05, -2.28041784e-05, -1.24076878e-05, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan]) - KT(zeuc)float64nan nan nan nan ... nan nan nan nan
- long_name :
- KT
- units :
- °C²/s
- standard_name :
- ocean_vertical_heat_diffusivity
array([ nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 1.75063220e-06, 1.54973389e-06, 3.94160465e-05, 1.79327457e-05, 1.91694538e-05, 1.61177356e-05, 1.34758569e-05, 8.34794199e-06, 1.77731359e-05, 1.36453579e-05, 2.44436893e-05, 8.29435941e-05, 2.50670786e-05, 4.22781901e-05, 5.70193132e-05, 8.96077685e-05, 2.36816586e-04, 7.67799528e-05, 6.23427545e-06, 4.58393676e-06, 7.18443693e-06, 7.87097983e-06, 7.57361167e-06, 4.88274066e-06, 9.00127011e-05, 9.29402391e-04, 8.37179242e-04, 4.12015094e-04, 6.52606163e-04, 4.13778009e-03, 1.80348034e-03, 1.03274985e-03, 1.12324553e-03, 2.55541215e-03, 2.92800330e-03, 3.66941342e-03, 4.63773320e-03, 6.74971157e-03, 3.11262206e-02, 3.26273440e-02, 8.56047758e-02, 2.53505751e-01, 9.96320593e-02, 2.39894248e-01, 2.21295658e-01, 5.28377288e-01, 5.10005861e-01, 2.47290622e+00, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan]) - ν(zeuc)float64nan nan nan nan ... nan nan nan nan
- long_name :
- ν
- units :
- W/kg
- standard_name :
- ocean_vertical_momentum_diffusivity
array([ nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 4.31541293e-05, 1.87475808e-04, 1.60830071e-04, 1.20034123e-04, 9.80373288e-05, 1.45866751e-04, 1.03845767e-04, 1.00173097e-04, 8.12388520e-05, 7.24120615e-05, 5.18961109e-05, 4.52475903e-05, 4.29103329e-05, 4.23408320e-05, 5.03857383e-05, 7.92359774e-05, 9.48361491e-05, 8.97218275e-05, 6.08520635e-05, 1.00874286e-04, 1.38767213e-04, 1.17102769e-04, 1.36578413e-04, 2.22404547e-04, 3.77859350e-04, 6.89249517e-04, 1.10802370e-03, 1.48866195e-03, 1.86612661e-03, 2.20730047e-03, 2.60012444e-03, 3.17856744e-03, 3.98798150e-03, 4.76411095e-03, 6.05826586e-03, 7.16354304e-03, 8.42592594e-03, 1.09242844e-02, 1.25466474e-02, 1.57281395e-02, 1.80557653e-02, 2.37917973e-02, 3.90604785e-02, 3.64038253e-02, 1.34412384e-01, 3.49902482e-02, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan])
- starttime :
- ['Time:20:34:29 298 ' 'Time:20:42:18 298 ' 'Time:20:52:14 298 ' ... 'Time:18:56:50 313 ' 'Time:19:04:01 313 ' 'Time:19:11:46 313 ']
- endtime :
- ['Time:20:38:29 298 ' 'Time:20:46:29 298 ' 'Time:20:56:29 298 ' ... 'Time:19:01:00 313 ' 'Time:19:08:40 313 ' 'Time:19:16:00 313 ']
- name :
- EQUIX
<xarray.DatasetView> Dimensions: (zeuc: 80) Coordinates: * zeuc (zeuc) float64 -200.0 -195.0 -190.0 -185.0 ... 185.0 190.0 195.0 Data variables: u (zeuc) float64 nan nan nan nan nan nan ... nan nan nan nan nan nan v (zeuc) float64 nan nan nan nan nan nan ... nan nan nan nan nan nan chi (zeuc) float64 nan nan nan nan nan nan ... nan nan nan nan nan nan eps (zeuc) float64 nan nan nan nan nan nan ... nan nan nan nan nan nan theta (zeuc) float64 nan nan nan nan nan nan ... nan nan nan nan nan nan Sh2 (zeuc) float64 nan nan nan nan nan nan ... nan nan nan nan nan nan Tz (zeuc) float64 nan nan nan nan nan nan ... nan nan nan nan nan nan N2T (zeuc) float64 nan nan nan nan nan nan ... nan nan nan nan nan nan Rig_T (zeuc) float64 nan nan nan nan nan nan ... nan nan nan nan nan nan Shred2 (zeuc) float64 nan nan nan nan nan nan ... nan nan nan nan nan nan KT (zeuc) float64 nan nan nan nan nan nan ... nan nan nan nan nan nan ν (zeuc) float64 nan nan nan nan nan nan ... nan nan nan nan nan nan Attributes: starttime: ['Time:20:34:29 298 ' 'Time:20:42:18 298 ' 'Time:20:52:14... endtime: ['Time:20:38:29 298 ' 'Time:20:46:29 298 ' 'Time:20:56:29... name: EQUIXEQUIX
Timescales of comparison#
Comparing a coarse climate model at 2/3° lateral grid spacing, and a time step of an hour, to microstructure observations that capture temperature variability at 7-15Hz is challenging.
The equator is a special place with strong diurnal variability in turbulence.
Capturing the daily timescale at the equator has been a vital benchmark for the KPP scheme (Large and Gent, 1999).
Interestingly we find that all simulations here reproduce a qualitatively similar variability at the daily frequency, even though variance is depressed at sub-daily and super-daily frequencies.
TODO: Frequency spectra comparison#
Mean#
Mean State Variable Profiles#
A lot more shear, \(S, S^2\) just above the EUC when visc is turned down!
We are very slightly lower on \(S^2\), \(N_T^2\) in the top 80m, compare
TAO,kpp.lmd.004,new_baseline.kpp.lmd.004.Using the standard
Ri_c=0.3, so deeper KPP surface layer, decreases \(S^2\), \(N_T^2\) in the top 60m.new_baseline.kpp.lmd.004vsnew_baseline.kpp.lmd.005
for context, Peters et al (1995) is interesting:
Variability patterns at 50-350 m are distinctly different from the upper 50 m containing the diurnal cycle of mixing
Large-scale shear of wavenumbers k < 0.01 cpm is associated with the slowly varying EUC and EIC. Large-scale is the dominant component of total shear above 50 m and in the thermostad, 170-270 m.
Fine-scale shear, 0.01cpm < k < 0.5 cpm,provides the dominant component of total rms shear and exceeds the large-scale component in thick layersaroundthe coresof EUC and EIC, where Fr < 1, at 50-170 m and below 270 m.
Variations of fine-scale shear cause variations in turbulent mixing; the large-scaleshear alone is a poor predictor of mixing.
Lacking a model that links large-scale, fine-scale, and turbulent flow components,our service to equatorial modelers consists of describing general levels of eddy diffusivities and their variability patterns.
These models should not be resolving “finescale” shear, and the mixing is not well correlated with “large-scale shear”, so we need a “background” diffusivity/viscosity to make things work.
But see that std(\(S^2\)) is a lot stronger above the EUC core, relative to TAO for new_baseline.kpp.lmd.004 and new_baseline.kpp.lmd.005
S2 = mixpods.plot_profile_fill(tree, "S2", "S^2")
N2 = mixpods.plot_profile_fill(tree, "N2T", "N_T^2")
u = mixpods.plot_profile_fill(tree, "sea_water_x_velocity", "u")
T = mixpods.plot_profile_fill(tree, "sea_water_potential_temperature", "T")
Ri = mixpods.plot_median_Ri(tree)
%autoreload
plot = (
(S2 + N2 + Ri + u + T)
.cols(5)
.opts(
hv.opts.Curve(xlim=(-350, 0), frame_height=600),
hv.opts.Layout(toolbar="left"),
hv.opts.Overlay(legend_offset=(0, 200)),
clone=True,
)
)
select_legends(plot, figure_index=4, legend_position="right")
/glade/u/home/dcherian/miniconda3/envs/pump/lib/python3.10/site-packages/holoviews/plotting/bokeh/plot.py:967: UserWarning: found multiple competing values for 'toolbar.active_drag' property; using the latest value
layout_plot = gridplot(
A deeper look at mixing below the EUC#
We find that tuning KPP’s shear scheme to better represent deep cycle turbulence by reducing \(Ri_0\) where median \(Ri_g\) is lower (0.25), prevents it from kicking in in the lower half of the EUC where median \(Ri_g\) is higher (1).
Microstructure observations from short term experiments (TIWE, EQUIX) do show higher viscosities \(ν \sim\) \SI{1e-4}{\meter\squared\per\second} below the EUC maximum.
We choose to preserve only TAO χpods above 90m depth, so there is very little data below the EUC core. For these data points, bins with less than 2000 hourly measurements are excluded.
With reduced \(Ri_0 \sim 0.5\), the KPP scheme is unable to reproduce these viscosities resulting a sharper EUC.
Peters et al (1995):
Fine-scale shear, 0.01cpm < k < 0.5 cpm,provides the dominant component of total rms shear and exceeds the large-scale component in thick layersaroundthe coresof EUC and EIC, where Fr < 1, at 50-170 m and below 270 m.
Variations of fine-scale shear cause variations in turbulent mixing; the large-scale shear alone is a poor predictor of mixing.
These simulations should not be resolving “finescale” shear, and the mixing is not well correlated with “large-scale shear”, so we need a “background” diffusivity/viscosity to make things work.
Indeed we see that additional background viscosity is necessary — compare new_baseline.hb to new_baseline.kpp.lmd.004.
tao_chi_counts = euc["TAO/chi"].count("time").load()
tao_chi_counts.hvplot.line(title="TAO χ number of hourly obs in bin")
euc_mean["TAO"] = euc_mean["TAO"].where(tao_chi_counts > 2000)
h = {
varname: mixpods.map_hvplot(
lambda node, name, muted: (
node.ds.cf[varname]
.reset_coords(drop=True)
.hvplot.line(
ylabel=varname,
label=name,
logx=varname != "sea_water_x_velocity",
invert=True,
)
),
euc_mean,
)
for varname in [
"sea_water_x_velocity",
"sea_water_potential_temperature",
"chi",
"eps",
"ocean_vertical_heat_diffusivity",
"ocean_vertical_momentum_diffusivity",
]
}
h["chi"].opts(ylim=(1e-9, 1e-4))
h["eps"].opts(ylim=(1e-9, 1e-4))
h["ocean_vertical_heat_diffusivity"].opts(ylim=(5e-7, 3))
h["ocean_vertical_momentum_diffusivity"].opts(ylim=(1e-6, 1e-1))
h2 = {
varname: mixpods.map_hvplot(
lambda node, name, muted: node.ds[varname]
.reset_coords(drop=True)
.hvplot.line(ylabel=varname, label=name, logx=False, invert=True),
euc_mean,
)
for varname in ["Rig_T"]
}
h
plot = (
hv.Layout(list(h.values()) + [h2["Rig_T"].opts(ylim=(0, 3))])
.opts(
hv.opts.Curve(frame_width=150, frame_height=300, xlim=(-150, 150)),
hv.opts.Layout(shared_axes=True),
hv.opts.Overlay(show_legend=True, show_grid=True, legend_position="right"),
*mixpods.HV_TOOLS_OPTIONS,
*mixpods.PRESENTATION_OPTS,
)
.cols(4)
)
select_legends(plot, figure_index=3, legend_position="right")
/glade/u/home/dcherian/miniconda3/envs/pump/lib/python3.10/site-packages/holoviews/plotting/bokeh/plot.py:967: UserWarning: found multiple competing values for 'toolbar.active_drag' property; using the latest value
layout_plot = gridplot(
Mean Turbulence Variable Profiles#
mixpods.map_hvplot(
lambda ds, name, muted: ds.ds.cf["ocean_vertical_heat_diffusivity"]
.mean("time")
.hvplot.line(label=name, muted=muted),
tree,
).opts(
ylim=(1e-8, 1e1),
legend_position="right",
logx=True,
invert_axes=True,
frame_width=300,
aspect=1 / 3,
)
WARNING:param.OverlayPlot211169: Due to internal constraints, when aspect and width/height is set, the bokeh backend uses those values as frame_width/frame_height instead. This ensures the aspect is respected, but means that the plot might be slightly larger than anticipated. Set the frame_width/frame_height explicitly to suppress this warning.
WARNING:param.OverlayPlot211169: aspect value was ignored because absolute width and height values were provided. Either supply explicit frame_width and frame_height to achieve desired aspect OR supply a combination of width or height and an aspect value.
WARNING:param.OverlayPlot211290: Due to internal constraints, when aspect and width/height is set, the bokeh backend uses those values as frame_width/frame_height instead. This ensures the aspect is respected, but means that the plot might be slightly larger than anticipated. Set the frame_width/frame_height explicitly to suppress this warning.
WARNING:param.OverlayPlot211290: aspect value was ignored because absolute width and height values were provided. Either supply explicit frame_width and frame_height to achieve desired aspect OR supply a combination of width or height and an aspect value.
mixpods.map_hvplot(
lambda ds, name, muted: ds["eps"]
.mean("time")
.load()
.hvplot.line(label=name, muted=muted),
tree,
).opts(
ylim=(1e-10, None),
legend_position="right",
logx=True,
invert_axes=True,
frame_width=300,
aspect=1 / 3,
)
WARNING:param.OverlayPlot212317: Due to internal constraints, when aspect and width/height is set, the bokeh backend uses those values as frame_width/frame_height instead. This ensures the aspect is respected, but means that the plot might be slightly larger than anticipated. Set the frame_width/frame_height explicitly to suppress this warning.
WARNING:param.OverlayPlot212317: aspect value was ignored because absolute width and height values were provided. Either supply explicit frame_width and frame_height to achieve desired aspect OR supply a combination of width or height and an aspect value.
WARNING:param.OverlayPlot212438: Due to internal constraints, when aspect and width/height is set, the bokeh backend uses those values as frame_width/frame_height instead. This ensures the aspect is respected, but means that the plot might be slightly larger than anticipated. Set the frame_width/frame_height explicitly to suppress this warning.
WARNING:param.OverlayPlot212438: aspect value was ignored because absolute width and height values were provided. Either supply explicit frame_width and frame_height to achieve desired aspect OR supply a combination of width or height and an aspect value.
mixpods.map_hvplot(
lambda ds, name, muted: ds.ds.cf["ocean_vertical_heat_diffusivity"]
.mean("time")
.load()
.hvplot.line(label=name, muted=muted),
tree,
).opts(
ylim=(1e-8, 1e1),
legend_position="right",
logx=True,
invert_axes=True,
frame_width=300,
aspect=1 / 3,
)
WARNING:param.OverlayPlot213366: Due to internal constraints, when aspect and width/height is set, the bokeh backend uses those values as frame_width/frame_height instead. This ensures the aspect is respected, but means that the plot might be slightly larger than anticipated. Set the frame_width/frame_height explicitly to suppress this warning.
WARNING:param.OverlayPlot213366: aspect value was ignored because absolute width and height values were provided. Either supply explicit frame_width and frame_height to achieve desired aspect OR supply a combination of width or height and an aspect value.
WARNING:param.OverlayPlot213467: Due to internal constraints, when aspect and width/height is set, the bokeh backend uses those values as frame_width/frame_height instead. This ensures the aspect is respected, but means that the plot might be slightly larger than anticipated. Set the frame_width/frame_height explicitly to suppress this warning.
WARNING:param.OverlayPlot213467: aspect value was ignored because absolute width and height values were provided. Either supply explicit frame_width and frame_height to achieve desired aspect OR supply a combination of width or height and an aspect value.
Turbulence Variable distributions#
handles = [
mixpods.plot_distributions(tree, "chi", bins=np.linspace(-11, -4, 101), log=True),
mixpods.plot_distributions(tree, "eps", bins=np.linspace(-11, -4, 101), log=True),
mixpods.plot_distributions(
tree, "ocean_vertical_heat_diffusivity", bins=np.linspace(-8, -1, 101), log=True
),
# plot_distributions(tree, "Jq", bins=np.linspace(-1000, 0, 51), log=False),
mixpods.plot_distributions(tree, "Rig_T", np.linspace(-0.5, 1.5, 61))
* hv.VLine(0.25).opts(line_color="k"),
]
/glade/u/home/dcherian/miniconda3/envs/pump/lib/python3.10/site-packages/xarray/core/computation.py:761: RuntimeWarning: invalid value encountered in log10
result_data = func(*input_data)
/glade/u/home/dcherian/miniconda3/envs/pump/lib/python3.10/site-packages/xarray/core/computation.py:761: RuntimeWarning: invalid value encountered in log10
result_data = func(*input_data)
plot = (
hv.Layout(handles)
.opts("Overlay", frame_width=500)
.cols(2)
.opts(*mixpods.HV_TOOLS_OPTIONS)
)
select_legends(plot, figure_index=1, legend_position="right")
/glade/u/home/dcherian/miniconda3/envs/pump/lib/python3.10/site-packages/holoviews/plotting/bokeh/plot.py:967: UserWarning: found multiple competing values for 'toolbar.active_drag' property; using the latest value
layout_plot = gridplot(
The daily timescale#
Daily composites (Moum et al, 2023)#
Hard to interpret! I think a lot of this is bias in KPP surface layer vs actively mixing layer in obs. We can write better diagnostics to check this (Moum et al, 2023)
The 89m χpod comparison is quite interesting. Suggests we do need more background visc
plot
%autoreload
plot = mixpods.plot_daily_composites(dailies, ["eps"], logy=True)
plot.opts(toolbar="left", show_legend=True)
%autoreload
mixpods.plot_daily_composites(
dailies, ["S2", "N2", "Rig_T"], logy=False, legend=False
).opts(hv.opts.GridSpace(show_legend=True, width=200), hv.opts.Overlay(frame_width=200))
Daily composites: boundary layer depth#
mixing_layer_depth_criteria = {
"boundary_layer_depth": {"name": "KPPhbl|KPP_OBLdepth|ePBL_h_ML"},
}
hbl = (
tree.drop_nodes("TAO")
.dc.subset_nodes("KPP_OBLdepth")
.dc.concatenate_nodes()
.reset_coords(drop=True)
).load()
(
# hbl.groupby("time.hour").mean().hvplot.line(by="node", flip_yaxis=True)
hbl.groupby("time.hour")
.mean()
.hvplot.line(by="node", flip_yaxis=True)
.opts(show_legend=False)
+ hbl.to_dataframe().hvplot.hist(
by="node",
bins=np.arange(0, 90, 1),
normed=1,
alpha=0.3,
ylim=(0, 0.05),
muted_alpha=0,
)
).opts(
hv.opts.Curve(invert_yaxis=True),
*mixpods.HV_TOOLS_OPTIONS,
*mixpods.PRESENTATION_OPTS,
)
Seasonal timescale#
Seasonal mean profiles#
Heat Budget#
f, ax = plt.subplots(
2,
math.ceil(len(tree) / 2),
constrained_layout=True,
squeeze=False,
sharex=True,
sharey=True,
figsize=(10, 3),
)
for axx, (name, node) in zip(ax.flat, tree.children.items()):
mixpods.plot_climo_heat_budget_1d(node.ds, mxldepth=-40, penetration="mom", ax=axx)
axx.set_title(name)
dcpy.plots.clean_axes(ax)
ENSO Timescale#
S2, N2 histograms#
Stability Diagram#
~Major change in La-Nina Warming for the new-baseline runs. We need to check ONI closely.~
fixed by just using the obs ONI everywhere.
This is probably OK since it shouldn’t drift too far away.
And we now average over the same time periods.
It is a pain to estimate this with branch runs,
but could be done if we spliced in the SST from the baseline case
I still can’t reproduce the Warner and Moum (2019) figure where the El-Nino warming phase overlaps a lot less with La-Nina cooling
I’m only using data in the “deep cycle layer”.
have not matched vertical grid spacing yet.
could interpolate to TAO χpod depths
think about resampling to daily. Currently hourly.
think about instrumental error on vertical gradients for S2, N2;
if added to model, would it change the bottom tail.
%autoreload
mixpods.plot_stability_diagram_by_dataset(tree, nrows=2)
ε-Ri histograms#
%autoreload
mixpods.map_hvplot(
lambda dt, name, muted: mixpods.plot_eps_ri_hist(
dt["eps_ri"].load(), label=name, muted=muted
),
tree.children,
).opts(
hv.opts.Curve(ylim=(1e-8, 3e-6)),
hv.opts.GridSpace(show_legend=True),
hv.opts.Overlay(show_grid=True, legend_position="bottom_left"),
*mixpods.HV_TOOLS_OPTIONS,
)
/glade/u/home/dcherian/miniconda3/envs/pump/lib/python3.10/site-packages/holoviews/plotting/bokeh/plot.py:588: UserWarning: found multiple competing values for 'toolbar.active_drag' property; using the latest value
plot = gridplot(plots[::-1],